app.py

# ================================================================
# 教育大模型MIA攻防研究 - Gradio演示系统 终极防重叠版
# 1. 严格基于你发来的完整代码底座，一字不漏
# 2. 修复：散点图（Trade-off）图例移至左下角，增加点位透明度防遮挡
# 3. 修复：折线图（Trend）图例移至下方空白处，不再遮挡数据线和阴影
# ================================================================

import os
import json
import re
import numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from sklearn.metrics import roc_curve, roc_auc_score
import gradio as gr

BASE_DIR = os.path.dirname(os.path.abspath(__file__))

# ================================================================
# 数据加载 
# ================================================================
def load_json(path):
    full = os.path.join(BASE_DIR, path)
    with open(full, 'r', encoding='utf-8') as f:
        return json.load(f)

def clean_text(text):
    if not isinstance(text, str):
        return str(text)
    text = re.sub(r'[\U00010000-\U0010ffff]', '', text)
    text = re.sub(r'[\ufff0-\uffff]', '', text)
    text = re.sub(r'[\u200b-\u200f\u2028-\u202f\u2060-\u206f\ufeff]', '', text)
    return text.strip()

# 尝试加载数据，如果不存在则使用虚拟数据以确保运行
try:
    member_data = load_json("data/member.json")
    non_member_data = load_json("data/non_member.json")
    config = load_json("config.json")
    all_data = load_json("results/all_results.json")
    mia_results = all_data["mia_results"]
    perturb_results = all_data["perturbation_results"]
    utility_results = all_data["utility_results"]
    full_losses = all_data["full_losses"]
    model_name = config.get('model_name', 'Qwen/Qwen2.5-Math-1.5B-Instruct')
except FileNotFoundError:
    print("⚠️ 警告: 未找到数据文件。将使用虚拟数据进行演示。")
    member_data = [{"question": "Q"+str(i), "answer": "A"+str(i), "task_type": "calculation", "metadata": {"name": "Student"+str(i), "student_id": str(1000+i), "class": "Class A", "score": 90}} for i in range(1000)]
    non_member_data = [{"question": "Q"+str(i+1000), "answer": "A"+str(i+1000), "task_type": "word_problem", "metadata": {"name": "Student"+str(i+1000), "student_id": str(2000+i), "class": "Class B", "score": 85}} for i in range(1000)]
    model_name = "Demo Model"
    mia_results = {"baseline": {"auc": 0.6230, "attack_accuracy": 0.6055, "precision": 0.6779, "recall": 0.4020, "f1": 0.5047, "tpr_at_5fpr": 0.1850, "tpr_at_1fpr": 0.0930, "loss_gap": 0.0107, "member_loss_mean": 0.2494, "non_member_loss_mean": 0.2601, "member_loss_std": 0.03, "non_member_loss_std": 0.03}}
    perturb_results = {}
    utility_results = {"baseline": {"accuracy": 0.660}}
    full_losses = {"baseline": {"member_losses": np.random.normal(0.2494, 0.03, 1000).tolist(), "non_member_losses": np.random.normal(0.2601, 0.03, 1000).tolist()}}
    for e in [0.02, 0.05, 0.1, 0.2]:
        k = f"smooth_eps_{e}"
        mia_results[k] = {m: v*0.9 for m, v in mia_results["baseline"].items()} 
        utility_results[k] = {"accuracy": 0.660 + e*0.1}
    for s in [0.005, 0.01, 0.015, 0.02, 0.025, 0.03]:
        k = f"perturbation_{s}"
        perturb_results[k] = {m: v*0.85 for m, v in mia_results["baseline"].items()} 
        perturb_results[k]["member_loss_std"] = np.sqrt(0.03**2 + s**2)
        perturb_results[k]["non_member_loss_std"] = np.sqrt(0.03**2 + s**2)

# ================================================================
# 全局图表配置
# ================================================================
COLORS = {
    'bg': '#FFFFFF',           
    'panel': '#F5F7FA',        
    'grid': '#E2E8F0',         
    'text': '#1E293B',         
    'text_dim': '#64748B',     
    'accent': '#007AFF',       
    'accent2': '#5856D6',      
    'danger': '#FF3B30',       
    'success': '#34C759',      
    'warning': '#FF9500',      
    'baseline': '#8E8E93',     
    'ls_colors': ['#A0C4FF', '#70A1FF', '#478EFF', '#007AFF'], 
    'op_colors': ['#98F5E1', '#6EE7B7', '#34D399', '#10B981', '#059669', '#047857'], 
}
CHART_W = 14

def apply_light_style(fig, ax_or_axes):
    fig.patch.set_facecolor(COLORS['bg'])
    axes = ax_or_axes if hasattr(ax_or_axes, '__iter__') else [ax_or_axes]
    for ax in axes:
        ax.set_facecolor(COLORS['panel'])
        for spine in ax.spines.values():
            spine.set_color(COLORS['grid'])
            spine.set_linewidth(1)
        ax.tick_params(colors=COLORS['text_dim'], labelsize=10, width=1)
        ax.xaxis.label.set_color(COLORS['text'])
        ax.yaxis.label.set_color(COLORS['text'])
        ax.title.set_color(COLORS['text'])
        ax.title.set_fontweight('semibold')
        ax.grid(True, color=COLORS['grid'], alpha=0.6, linestyle='-', linewidth=0.8)
        ax.set_axisbelow(True) 

# ================================================================
# 使用标准 Unicode ε 和 σ
# ================================================================
LS_KEYS = ["baseline", "smooth_eps_0.02", "smooth_eps_0.05", "smooth_eps_0.1", "smooth_eps_0.2"]
LS_LABELS_PLOT = ["Baseline", "LS(ε=0.02)", "LS(ε=0.05)", "LS(ε=0.1)", "LS(ε=0.2)"]
LS_LABELS_MD = ["基线(Baseline)", "LS(ε=0.02)", "LS(ε=0.05)", "LS(ε=0.1)", "LS(ε=0.2)"]

OP_SIGMAS = [0.005, 0.01, 0.015, 0.02, 0.025, 0.03]
OP_KEYS = [f"perturbation_{s}" for s in OP_SIGMAS]
OP_LABELS_PLOT = [f"OP(σ={s})" for s in OP_SIGMAS]
OP_LABELS_MD = [f"OP(σ={s})" for s in OP_SIGMAS]

ALL_KEYS = LS_KEYS + OP_KEYS

def gm(key, metric, default=0):
    if key in mia_results: return mia_results[key].get(metric, default)
    if key in perturb_results: return perturb_results[key].get(metric, default)
    return default

def gu(key):
    if key in utility_results: return utility_results[key].get("accuracy", 0) * 100
    if key.startswith("perturbation_"): return utility_results.get("baseline", {}).get("accuracy", 0) * 100
    return 0

bl_auc = gm("baseline", "auc")
bl_acc = gu("baseline")
bl_m_mean = gm("baseline", "member_loss_mean")
bl_nm_mean = gm("baseline", "non_member_loss_mean")

TYPE_CN = {'calculation': '基础计算', 'word_problem': '应用题', 'concept': '概念问答', 'error_correction': '错题订正'}

np.random.seed(777)
EVAL_POOL = []
_types = ['calculation']*120 + ['word_problem']*90 + ['concept']*60 + ['error_correction']*30
for _i in range(300):
    _t = _types[_i]
    if _t == 'calculation':
        _a, _b = int(np.random.randint(10,500)), int(np.random.randint(10,500))
        _op = ['+','-','x'][_i%3]
        if _op=='+': _q,_ans=f"{_a} + {_b} = ?",str(_a+_b)
        elif _op=='-': _q,_ans=f"{_a} - {_b} = ?",str(_a-_b)
        else: _q,_ans=f"{_a} x {_b} = ?",str(_a*_b)
    elif _t == 'word_problem':
        _a,_b = int(np.random.randint(5,200)), int(np.random.randint(3,50))
        _tpls = [(f"{_a} apples, ate {_b}, left?",str(_a-_b)), (f"{_a} per group, {_b} groups, total?",str(_a*_b))]
        _q,_ans = _tpls[_i%len(_tpls)]
    elif _t == 'concept':
        _cs = [("area","Area = space occupied by a shape"),("perimeter","Perimeter = total boundary length")]
        _cn,_df = _cs[_i%len(_cs)]; _q,_ans = f"What is {_cn}?",_df
    else:
        _a,_b = int(np.random.randint(10,99)), int(np.random.randint(10,99))
        _w = _a+_b+int(np.random.choice([-1,1,-10,10]))
        _q,_ans = f"Student got {_a}+{_b}={_w}, correct?",str(_a+_b)
    item = {'question':_q,'answer':_ans,'type_cn':TYPE_CN[_t]}
    for key in LS_KEYS:
        acc = gu(key)/100; item[key] = bool(np.random.random()<acc)
    EVAL_POOL.append(item)

# ================================================================
# 图表绘制函数 (保留你的原貌，只做防重叠修复)
# ================================================================
def fig_gauge(loss_val, m_mean, nm_mean, thr, m_std, nm_std):
    fig, ax = plt.subplots(figsize=(10, 2.6)); fig.patch.set_facecolor(COLORS['bg']); ax.set_facecolor(COLORS['panel'])
    xlo = min(m_mean - 3.0 * m_std, loss_val - 0.005); xhi = max(nm_mean + 3.0 * nm_std, loss_val + 0.005)
    ax.axvspan(xlo, thr, alpha=0.2, color=COLORS['accent']); ax.axvspan(thr, xhi, alpha=0.2, color=COLORS['danger'])
    ax.axvline(m_mean, color=COLORS['accent'], lw=2, ls=':', alpha=0.8, zorder=2)
    ax.text(m_mean - 0.002, 1.02, f'Member Mean\n{m_mean:.4f}', ha='right', va='bottom', fontsize=9, color=COLORS['accent'], transform=ax.get_xaxis_transform())
    ax.axvline(nm_mean, color=COLORS['danger'], lw=2, ls=':', alpha=0.8, zorder=2)
    ax.text(nm_mean + 0.002, 1.02, f'Non-Member Mean\n{nm_mean:.4f}', ha='left', va='bottom', fontsize=9, color=COLORS['danger'], transform=ax.get_xaxis_transform())
    ax.axvline(thr, color=COLORS['text_dim'], lw=2.5, ls='--', zorder=3)
    ax.text(thr, 1.25, f'Threshold\n{thr:.4f}', ha='center', va='bottom', fontsize=10, fontweight='bold', color=COLORS['text_dim'], transform=ax.get_xaxis_transform())
    mc = COLORS['accent'] if loss_val < thr else COLORS['danger']
    ax.plot(loss_val, 0.5, marker='o', ms=16, color='white', mec=mc, mew=3, zorder=5, transform=ax.get_xaxis_transform())
    ax.text(loss_val, 0.75, f'Current Loss\n{loss_val:.4f}', ha='center', fontsize=11, fontweight='bold', color=mc, transform=ax.get_xaxis_transform())
    ax.text((xlo+thr)/2, 0.25, 'MEMBER', ha='center', fontsize=12, color=COLORS['accent'], alpha=0.6, fontweight='bold', transform=ax.get_xaxis_transform())
    ax.text((thr+xhi)/2, 0.25, 'NON-MEMBER', ha='center', fontsize=12, color=COLORS['danger'], alpha=0.6, fontweight='bold', transform=ax.get_xaxis_transform())
    ax.set_xlim(xlo, xhi); ax.set_yticks([])
    for s in ax.spines.values(): s.set_visible(False)
    ax.spines['bottom'].set_visible(True); ax.spines['bottom'].set_color(COLORS['grid']); ax.tick_params(colors=COLORS['text_dim'], width=1)
    ax.set_xlabel('Loss Value', fontsize=11, color=COLORS['text'], fontweight='medium'); plt.tight_layout(pad=0.5)
    return fig

def fig_auc_bar():
    names, vals, clrs = [], [], []
    ls_c = [COLORS['baseline']] + COLORS['ls_colors']
    for i,(k,l) in enumerate(zip(LS_KEYS, LS_LABELS_PLOT)):
        if k in mia_results: names.append(l); vals.append(mia_results[k]['auc']); clrs.append(ls_c[i])
    for i,(k,l) in enumerate(zip(OP_KEYS, OP_LABELS_PLOT)):
        if k in perturb_results: names.append(l); vals.append(perturb_results[k]['auc']); clrs.append(COLORS['op_colors'][i])
    fig, ax = plt.subplots(figsize=(14, 6)); apply_light_style(fig, ax)
    bars = ax.bar(range(len(names)), vals, color=clrs, width=0.65, edgecolor='none', zorder=3)
    for b,v in zip(bars, vals): ax.text(b.get_x()+b.get_width()/2, v+0.01, f'{v:.4f}', ha='center', fontsize=10, fontweight='semibold', color=COLORS['text'])
    ax.axhline(0.5, color=COLORS['text_dim'], ls='--', lw=1.5, alpha=0.6, label='Random Guess (0.5)', zorder=2)
    ax.axhline(bl_auc, color=COLORS['danger'], ls=':', lw=1.5, alpha=0.8, label=f'Baseline ({bl_auc:.4f})', zorder=2)
    ax.set_ylabel('MIA Attack AUC', fontsize=12, fontweight='medium'); ax.set_title('Defense Effectiveness: MIA AUC Comparison', fontsize=14, fontweight='bold', pad=20)
    ax.set_ylim(0.45, max(vals)+0.05); ax.set_xticks(range(len(names))); ax.set_xticklabels(names, rotation=30, ha='right', fontsize=11)
    ax.legend(facecolor=COLORS['bg'], edgecolor='none', labelcolor=COLORS['text'], fontsize=10, loc='upper right'); plt.tight_layout()
    return fig

def fig_radar():
    ms = ['AUC', 'Atk Acc', 'Prec', 'Recall', 'F1', 'TPR@5%', 'TPR@1%', 'Gap']
    mk = ['auc', 'attack_accuracy', 'precision', 'recall', 'f1', 'tpr_at_5fpr', 'tpr_at_1fpr', 'loss_gap']
    N = len(ms); ag = np.linspace(0, 2 * np.pi, N, endpoint=False).tolist() + [0]
    fig, axes = plt.subplots(1, 2, figsize=(CHART_W + 2, 7), subplot_kw=dict(polar=True)); fig.patch.set_facecolor('white')

    ls_cfgs = [("Baseline", "baseline", '#F04438'), ("LS(ε=0.02)", "smooth_eps_0.02", '#B2DDFF'), ("LS(ε=0.05)", "smooth_eps_0.05", '#84CAFF'), ("LS(ε=0.1)", "smooth_eps_0.1", '#2E90FA'), ("LS(ε=0.2)", "smooth_eps_0.2", '#7A5AF8')]
    op_cfgs = [("Baseline", "baseline", '#F04438'), ("OP(σ=0.005)", "perturbation_0.005", '#A6F4C5'), ("OP(σ=0.01)", "perturbation_0.01", '#6CE9A6'), ("OP(σ=0.015)", "perturbation_0.015", '#32D583'), ("OP(σ=0.02)", "perturbation_0.02", '#12B76A'), ("OP(σ=0.025)", "perturbation_0.025", '#039855'), ("OP(σ=0.03)", "perturbation_0.03", '#027A48')]

    # 🌟 核心修改：全局归一化，锁定统一的天花板
    all_configs_keys = ["baseline"] + LS_KEYS[1:] + OP_KEYS
    mx_global = [max(gm(k, m_key) for k in all_configs_keys) for m_key in mk]
    mx_global = [m if m > 0 else 1 for m in mx_global]

    for ax_idx, (ax, cfgs, title) in enumerate([(axes[0], ls_cfgs, 'Radar Analysis: Label Smoothing'), (axes[1], op_cfgs, 'Radar Analysis: Output Perturbation')]):
        ax.set_facecolor('white')
        for nm, ky, cl in cfgs:
            # 🌟 这里统一除以 mx_global，确保红线形状完全一致
            v = [gm(ky, m_key) / mx_global[i] for i, m_key in enumerate(mk)]; v += [v[0]]
            ax.plot(ag, v, 'o-', lw=2.8 if ky == 'baseline' else 1.8, label=nm, color=cl, ms=5, alpha=0.95 if ky == 'baseline' else 0.85)
            ax.fill(ag, v, alpha=0.10 if ky == 'baseline' else 0.04, color=cl)
        ax.set_xticks(ag[:-1]); ax.set_xticklabels(ms, fontsize=10, color=COLORS['text']); ax.set_yticklabels([])
        ax.set_title(title, fontsize=14, fontweight='bold', color=COLORS['text'], pad=25)
        ax.legend(loc='upper right', bbox_to_anchor=(1.35 if ax_idx == 1 else 1.30, 1.12), fontsize=9, framealpha=0.9, edgecolor=COLORS['grid'])
        ax.spines['polar'].set_color(COLORS['grid']); ax.grid(color=COLORS['grid'], alpha=0.5)
    plt.tight_layout()
    return fig

# 🌟 修复：这是我专门加的 3联 Loss 直方图横向对比
def fig_d3_dist_compare():
    configs = [
        ("Baseline (No Defense)", "baseline", COLORS['danger'], None),
        ("Label Smoothing (ε=0.2)", "smooth_eps_0.2", COLORS['accent2'], None),
        ("Output Perturbation (σ=0.03)", "baseline", COLORS['success'], 0.03),
    ]
    fig, axes = plt.subplots(1, 3, figsize=(18, 5.5))
    apply_light_style(fig, axes)

    for idx, (title, key, color, sigma) in enumerate(configs):
        ax = axes[idx]
        if key in full_losses:
            m_losses = np.array(full_losses[key]['member_losses'])
            nm_losses = np.array(full_losses[key]['non_member_losses'])
            if sigma:
                rm=np.random.RandomState(42); rn=np.random.RandomState(137)
                m_losses = m_losses + rm.normal(0, sigma, len(m_losses))
                nm_losses = nm_losses + rn.normal(0, sigma, len(nm_losses))
            all_v = np.concatenate([m_losses, nm_losses])
            bins = np.linspace(all_v.min(), all_v.max(), 35)
            ax.hist(m_losses, bins=bins, alpha=0.6, color=COLORS['accent'], label='Member', density=True, edgecolor='white')
            ax.hist(nm_losses, bins=bins, alpha=0.6, color=COLORS['danger'], label='Non-Member', density=True, edgecolor='white')
            
            m_mean = np.mean(m_losses); nm_mean = np.mean(nm_losses)
            gap = nm_mean - m_mean
            ax.axvline(m_mean, color=COLORS['accent'], ls='--', lw=2, alpha=0.8)
            ax.axvline(nm_mean, color=COLORS['danger'], ls='--', lw=2, alpha=0.8)
            ax.annotate(f'Gap={gap:.4f}', xy=((m_mean+nm_mean)/2, ax.get_ylim()[1]*0.85 if ax.get_ylim()[1]>0 else 5),
                       fontsize=11, fontweight='bold', color=color, ha='center',
                       bbox=dict(boxstyle='round,pad=0.4', fc='white', ec=color, alpha=0.9))
        
        ax.set_title(title, fontsize=13, fontweight='bold', color=color, pad=15)
        ax.set_xlabel('Loss', fontsize=12)
        if idx == 0: ax.set_ylabel('Density', fontsize=12)
        ax.legend(fontsize=10, facecolor=COLORS['bg'], edgecolor='none')
        
    fig.suptitle('Loss Distribution: Baseline vs LS vs OP', fontsize=16, fontweight='bold', color=COLORS['text'], y=1.05)
    plt.tight_layout(); return fig

def fig_loss_dist():
    items = [(k, l, gm(k, 'auc')) for k, l in zip(LS_KEYS, LS_LABELS_PLOT) if k in full_losses]; n = len(items)
    if n == 0: return plt.figure()
    fig, axes = plt.subplots(1, n, figsize=(4.5*n, 4.5)); axes = [axes] if n == 1 else axes; apply_light_style(fig, axes)
    for ax, (k, l, a) in zip(axes, items):
        m = full_losses[k]['member_losses']; nm = full_losses[k]['non_member_losses']; bins = np.linspace(min(min(m),min(nm)), max(max(m),max(nm)), 30)
        ax.hist(m, bins=bins, alpha=0.6, color=COLORS['accent'], label='Member', density=True, edgecolor='white')
        ax.hist(nm, bins=bins, alpha=0.6, color=COLORS['danger'], label='Non-Member', density=True, edgecolor='white')
        ax.set_title(f'{l}\nAUC={a:.4f}', fontsize=11, fontweight='semibold'); ax.set_xlabel('Loss', fontsize=10); ax.set_ylabel('Density', fontsize=10)
        ax.legend(fontsize=9, facecolor=COLORS['bg'], edgecolor='none', labelcolor=COLORS['text'])
    plt.tight_layout(); return fig

def fig_perturb_dist():
    if 'baseline' not in full_losses: return plt.figure()
    ml = np.array(full_losses['baseline']['member_losses']); nl = np.array(full_losses['baseline']['non_member_losses'])
    fig, axes = plt.subplots(2, 3, figsize=(16, 9)); axes_flat = axes.flatten(); apply_light_style(fig, axes_flat)
    for i, (ax, s) in enumerate(zip(axes_flat, OP_SIGMAS)):
        rng_m = np.random.RandomState(42); rng_nm = np.random.RandomState(137)
        mp = ml + rng_m.normal(0, s, len(ml)); np_ = nl + rng_nm.normal(0, s, len(nl)); v = np.concatenate([mp, np_])
        bins = np.linspace(v.min(), v.max(), 28)
        ax.hist(mp, bins=bins, alpha=0.6, color=COLORS['accent'], label='Mem+noise', density=True, edgecolor='white')
        ax.hist(np_, bins=bins, alpha=0.6, color=COLORS['danger'], label='Non+noise', density=True, edgecolor='white')
        pa = gm(f'perturbation_{s}', 'auc')
        ax.set_title(f'OP(σ={s})\nAUC={pa:.4f}', fontsize=11, fontweight='semibold'); ax.set_xlabel('Loss', fontsize=10)
        ax.legend(fontsize=9, facecolor=COLORS['bg'], edgecolor='none', labelcolor=COLORS['text'])
    plt.tight_layout(); return fig

def fig_roc_curves():
    fig, axes = plt.subplots(1, 2, figsize=(16, 7)); apply_light_style(fig, axes)
    ax = axes[0]; ls_colors = [COLORS['danger'], COLORS['ls_colors'][0], COLORS['ls_colors'][1], COLORS['ls_colors'][2], COLORS['ls_colors'][3]]
    for i, (k, l) in enumerate(zip(LS_KEYS, LS_LABELS_PLOT)):
        if k not in full_losses: continue
        m = np.array(full_losses[k]['member_losses']); nm = np.array(full_losses[k]['non_member_losses'])
        y_true = np.concatenate([np.ones(len(m)), np.zeros(len(nm))]); y_scores = np.concatenate([-m, -nm])
        fpr, tpr, _ = roc_curve(y_true, y_scores); auc_val = roc_auc_score(y_true, y_scores)
        ax.plot(fpr, tpr, color=ls_colors[i], lw=2.5, label=f'{l} (AUC={auc_val:.4f})')
    ax.plot([0,1], [0,1], '--', color=COLORS['text_dim'], lw=1.5, label='Random')
    ax.set_xlabel('False Positive Rate', fontsize=12, fontweight='medium'); ax.set_ylabel('True Positive Rate', fontsize=12, fontweight='medium'); ax.set_title('ROC Curves: Label Smoothing', fontsize=14, fontweight='bold', pad=15); ax.legend(fontsize=10, facecolor=COLORS['bg'], edgecolor='none', labelcolor=COLORS['text'])
    
    ax = axes[1]
    if 'baseline' in full_losses:
        ml_base = np.array(full_losses['baseline']['member_losses']); nl_base = np.array(full_losses['baseline']['non_member_losses']); y_true = np.concatenate([np.ones(len(ml_base)), np.zeros(len(nl_base))]); y_scores = np.concatenate([-ml_base, -nl_base])
        fpr, tpr, _ = roc_curve(y_true, y_scores); ax.plot(fpr, tpr, color=COLORS['danger'], lw=2.5, label=f'Baseline (AUC={bl_auc:.4f})')
        for i, s in enumerate(OP_SIGMAS):
            rng_m = np.random.RandomState(42); rng_nm = np.random.RandomState(137); mp = ml_base + rng_m.normal(0, s, len(ml_base)); np_ = nl_base + rng_nm.normal(0, s, len(nl_base)); y_scores_p = np.concatenate([-mp, -np_]); fpr_p, tpr_p, _ = roc_curve(y_true, y_scores_p); auc_p = roc_auc_score(y_true, y_scores_p)
            ax.plot(fpr_p, tpr_p, color=COLORS['op_colors'][i], lw=2, label=f'OP(σ={s}) (AUC={auc_p:.4f})')
    ax.plot([0,1], [0,1], '--', color=COLORS['text_dim'], lw=1.5, label='Random')
    ax.set_xlabel('False Positive Rate', fontsize=12, fontweight='medium'); ax.set_ylabel('True Positive Rate', fontsize=12, fontweight='medium'); ax.set_title('ROC Curves: Output Perturbation', fontsize=14, fontweight='bold', pad=15); ax.legend(fontsize=10, facecolor=COLORS['bg'], edgecolor='none', labelcolor=COLORS['text'], loc='lower right'); plt.tight_layout()
    return fig

def fig_tpr_at_low_fpr():
    fig, axes = plt.subplots(1, 2, figsize=(16, 6.5)); apply_light_style(fig, axes); labels_all, tpr5_all, tpr1_all, colors_all = [], [], [], []; ls_c = [COLORS['baseline']] + COLORS['ls_colors']
    for i, (k, l) in enumerate(zip(LS_KEYS, LS_LABELS_PLOT)): labels_all.append(l); tpr5_all.append(gm(k, 'tpr_at_5fpr')); tpr1_all.append(gm(k, 'tpr_at_1fpr')); colors_all.append(ls_c[i])
    for i, (k, l) in enumerate(zip(OP_KEYS, OP_LABELS_PLOT)): labels_all.append(l); tpr5_all.append(gm(k, 'tpr_at_5fpr')); tpr1_all.append(gm(k, 'tpr_at_1fpr')); colors_all.append(COLORS['op_colors'][i])
    x = range(len(labels_all)); ax = axes[0]; bars = ax.bar(x, tpr5_all, color=colors_all, width=0.65, edgecolor='none', zorder=3)
    for b, v in zip(bars, tpr5_all): ax.text(b.get_x()+b.get_width()/2, v+0.005, f'{v:.3f}', ha='center', fontsize=9, fontweight='semibold', color=COLORS['text'])
    ax.set_ylabel('TPR @ 5% FPR', fontsize=12, fontweight='medium'); ax.set_title('Attack Power at 5% FPR', fontsize=14, fontweight='bold', pad=15); ax.set_xticks(x); ax.set_xticklabels(labels_all, rotation=35, ha='right', fontsize=11); ax.axhline(0.05, color=COLORS['warning'], ls='--', lw=1.5, alpha=0.7, label='Random (0.05)'); ax.legend(facecolor=COLORS['bg'], edgecolor='none', labelcolor=COLORS['text'], fontsize=10)
    
    ax = axes[1]; bars = ax.bar(x, tpr1_all, color=colors_all, width=0.65, edgecolor='none', zorder=3)
    for b, v in zip(bars, tpr1_all): ax.text(b.get_x()+b.get_width()/2, v+0.003, f'{v:.3f}', ha='center', fontsize=9, fontweight='semibold', color=COLORS['text'])
    ax.set_ylabel('TPR @ 1% FPR', fontsize=12, fontweight='medium'); ax.set_title('Attack Power at 1% FPR (Strict)', fontsize=14, fontweight='bold', pad=15); ax.set_xticks(x); ax.set_xticklabels(labels_all, rotation=35, ha='right', fontsize=11); ax.axhline(0.01, color=COLORS['warning'], ls='--', lw=1.5, alpha=0.7, label='Random (0.01)'); ax.legend(facecolor=COLORS['bg'], edgecolor='none', labelcolor=COLORS['text'], fontsize=10); plt.tight_layout()
    return fig

def fig_loss_gap_waterfall():
    fig, ax = plt.subplots(figsize=(14, 6)); apply_light_style(fig, ax); names, gaps, clrs = [], [], []; ls_c = [COLORS['baseline']] + COLORS['ls_colors']
    for i, (k, l) in enumerate(zip(LS_KEYS, LS_LABELS_PLOT)): names.append(l); gaps.append(gm(k, 'loss_gap')); clrs.append(ls_c[i])
    for i, (k, l) in enumerate(zip(OP_KEYS, OP_LABELS_PLOT)): names.append(l); gaps.append(gm(k, 'loss_gap')); clrs.append(COLORS['op_colors'][i])
    bars = ax.bar(range(len(names)), gaps, color=clrs, width=0.65, edgecolor='none', zorder=3)
    for b, v in zip(bars, gaps): ax.text(b.get_x()+b.get_width()/2, v+0.0005, f'{v:.4f}', ha='center', fontsize=10, fontweight='semibold', color=COLORS['text'])
    ax.set_ylabel('Loss Gap', fontsize=12, fontweight='medium'); ax.set_title('Member vs Non-Member Loss Gap', fontsize=14, fontweight='bold', pad=20); ax.set_xticks(range(len(names))); ax.set_xticklabels(names, rotation=30, ha='right', fontsize=11); ax.annotate('Smaller gap = Better Privacy', xy=(8, gaps[0]*0.4), fontsize=11, color=COLORS['success'], fontstyle='italic', ha='center', backgroundcolor=COLORS['bg'], bbox=dict(boxstyle='round,pad=0.4', facecolor=COLORS['panel'], edgecolor=COLORS['success'], alpha=0.8)); plt.tight_layout()
    return fig

# 🌟 效用页面柱状图
def fig_acc_bar():
    names, vals, clrs = [], [], []; ls_c = [COLORS['baseline']] + COLORS['ls_colors']
    for i, (k, l) in enumerate(zip(LS_KEYS, LS_LABELS_PLOT)):
        if k in utility_results: names.append(l); vals.append(utility_results[k]['accuracy']*100); clrs.append(ls_c[i])
    for i, (k, l) in enumerate(zip(OP_KEYS, OP_LABELS_PLOT)):
        if k in perturb_results: names.append(l); vals.append(bl_acc); clrs.append(COLORS['op_colors'][i])
    fig, ax = plt.subplots(figsize=(12, 7)); apply_light_style(fig, ax); bars = ax.bar(range(len(names)), vals, color=clrs, width=0.65, edgecolor='none', zorder=3)
    for b, v in zip(bars, vals): ax.text(b.get_x()+b.get_width()/2, v+1, f'{v:.1f}%', ha='center', fontsize=11, fontweight='bold', color=COLORS['text'])
    ax.set_ylabel('Test Accuracy (%)', fontsize=12, fontweight='medium'); ax.set_title('Model Utility: Test Accuracy', fontsize=15, fontweight='bold', pad=20)
    ax.set_ylim(0, 105); ax.set_xticks(range(len(names))); ax.set_xticklabels(names, rotation=35, ha='right', fontsize=12); plt.tight_layout()
    return fig

# 🌟 修复：散点图增加透明度防遮挡，并且图例移至左下角空白处
def fig_tradeoff():
    fig, ax = plt.subplots(figsize=(12, 7)); apply_light_style(fig, ax); markers_ls = ['o', 's', 's', 's', 's']; ls_c = [COLORS['baseline']] + COLORS['ls_colors']
    for i, (k, l) in enumerate(zip(LS_KEYS, LS_LABELS_PLOT)):
        if k in mia_results and k in utility_results: ax.scatter(utility_results[k]['accuracy']*100, mia_results[k]['auc'], label=l, marker=markers_ls[i], color=ls_c[i], s=250, edgecolors='white', lw=2, zorder=5, alpha=0.9)
    op_markers = ['^', 'D', 'v', 'P', 'X', 'h']
    for i, (k, l) in enumerate(zip(OP_KEYS, OP_LABELS_PLOT)):
        # 增加透明度 alpha=0.75 防止叠放时遮盖
        if k in perturb_results: ax.scatter(bl_acc, perturb_results[k]['auc'], label=l, marker=op_markers[i], color=COLORS['op_colors'][i], s=200, edgecolors='white', lw=1.5, zorder=6, alpha=0.75)
    
    ax.axhline(0.5, color=COLORS['text_dim'], ls='--', alpha=0.6, label='Random (AUC=0.5)')
    ax.annotate('IDEAL ZONE\nHigh Utility, Low Risk', xy=(85, 0.51), fontsize=11, fontweight='bold', color=COLORS['success'], alpha=0.7, ha='center', backgroundcolor=COLORS['bg'])
    ax.annotate('HIGH RISK ZONE\nLow Utility, High Risk', xy=(62, 0.61), fontsize=11, fontweight='bold', color=COLORS['danger'], alpha=0.7, ha='center', backgroundcolor=COLORS['bg'])
    ax.set_xlabel('Model Utility (Accuracy %)', fontsize=12, fontweight='medium'); ax.set_ylabel('Privacy Risk (MIA AUC)', fontsize=12, fontweight='medium')
    ax.set_title('Privacy-Utility Trade-off Analysis', fontsize=15, fontweight='bold', pad=20)
    # 图例放到绝对安全的左下角空白区域
    ax.legend(fontsize=11, loc='lower left', ncol=2, facecolor=COLORS['bg'], edgecolor='none'); plt.tight_layout()
    return fig

# 🌟 修复：趋势图图例移位，避免遮挡数据线和阴影
def fig_auc_trend():
    fig, axes = plt.subplots(1, 2, figsize=(16, 6.5)); apply_light_style(fig, axes); ax = axes[0]; eps_vals = [0.0, 0.02, 0.05, 0.1, 0.2]; auc_vals = [gm(k, 'auc') for k in LS_KEYS]; acc_vals = [gu(k) for k in LS_KEYS]
    ax2 = ax.twinx(); line1 = ax.plot(eps_vals, auc_vals, 'o-', color=COLORS['danger'], lw=3, ms=9, label='MIA AUC (Risk)', zorder=5); line2 = ax2.plot(eps_vals, acc_vals, 's--', color=COLORS['accent'], lw=3, ms=9, label='Utility % (right)', zorder=5); ax.axhline(0.5, color=COLORS['text_dim'], ls=':', alpha=0.5)
    ax.fill_between(eps_vals, auc_vals, 0.5, alpha=0.08, color=COLORS['danger'])
    ax.set_xlabel('Label Smoothing ε', fontsize=12, fontweight='medium'); ax.set_ylabel('MIA AUC', fontsize=12, fontweight='medium', color=COLORS['danger']); ax2.set_ylabel('Utility (%)', fontsize=12, fontweight='medium', color=COLORS['accent']); ax.set_title('Label Smoothing Trends', fontsize=14, fontweight='bold', pad=15); ax.tick_params(axis='y', labelcolor=COLORS['danger']); ax2.tick_params(axis='y', labelcolor=COLORS['accent']); ax2.spines['right'].set_color(COLORS['accent']); ax2.spines['left'].set_color(COLORS['danger']); lines = line1 + line2; labels = [l.get_label() for l in lines]
    # 图例移至右下角安全区
    ax.legend(lines, labels, fontsize=10, facecolor=COLORS['bg'], edgecolor='none', loc='lower right')
    
    ax = axes[1]; sig_vals = OP_SIGMAS; auc_op = [gm(k, 'auc') for k in OP_KEYS]; ax.plot(sig_vals, auc_op, 'o-', color=COLORS['success'], lw=3, ms=9, zorder=5, label='MIA AUC'); ax.axhline(bl_auc, color=COLORS['danger'], ls='--', lw=2, alpha=0.6, label=f'Baseline ({bl_auc:.4f})'); ax.axhline(0.5, color=COLORS['text_dim'], ls=':', alpha=0.5, label='Random (0.5)'); ax.fill_between(sig_vals, auc_op, bl_auc, alpha=0.2, color=COLORS['success'], label='AUC Reduction')
    ax2r = ax.twinx(); ax2r.axhline(bl_acc, color=COLORS['success'], ls='-', lw=2.5, alpha=0.8); ax2r.set_ylabel(f'Utility = {bl_acc:.1f}% (unchanged)', fontsize=12, fontweight='medium', color=COLORS['success']); ax2r.set_ylim(0,100); ax2r.tick_params(axis='y', labelcolor=COLORS['success']); ax2r.spines['right'].set_color(COLORS['success'])
    ax.set_xlabel('Perturbation σ', fontsize=12, fontweight='medium'); ax.set_ylabel('MIA AUC', fontsize=12, fontweight='medium'); ax.set_title('Output Perturbation Trends', fontsize=14, fontweight='bold', pad=15)
    # 图例移至左下角安全区
    ax.legend(fontsize=10, facecolor=COLORS['bg'], edgecolor='none', loc='lower left'); plt.tight_layout()
    return fig

# ================================================================
# 回调函数 (维持你原有的所有核心逻辑不变)
# ================================================================
def cb_sample(src):
    pool = member_data if "训练集" in src else non_member_data
    s = pool[np.random.randint(len(pool))]
    m = s['metadata']
    md = f"""
    <table style="width:100%; border-collapse: collapse; border: 1px solid #E2E8F0; border-radius: 8px; overflow: hidden;">
        <tr style="background-color: #F9F9F9;">
            <th style="padding: 10px; text-align: left; color: #86868B; font-weight: 600; border-bottom: 1px solid #E2E8F0;">字段</th>
            <th style="padding: 10px; text-align: left; color: #86868B; font-weight: 600; border-bottom: 1px solid #E2E8F0;">值</th>
        </tr>
        <tr><td style="padding: 10px; color: #1D1D1F; border-bottom: 1px solid #E2E8F0;">姓名</td><td style="padding: 10px; color: #1D1D1F; border-bottom: 1px solid #E2E8F0;">{clean_text(str(m.get('name','')))}</td></tr>
        <tr><td style="padding: 10px; color: #1D1D1F; border-bottom: 1px solid #E2E8F0;">学号</td><td style="padding: 10px; color: #1D1D1F; border-bottom: 1px solid #E2E8F0;">{clean_text(str(m.get('student_id','')))}</td></tr>
        <tr><td style="padding: 10px; color: #1D1D1F; border-bottom: 1px solid #E2E8F0;">班级</td><td style="padding: 10px; color: #1D1D1F; border-bottom: 1px solid #E2E8F0;">{clean_text(str(m.get('class','')))}</td></tr>
        <tr><td style="padding: 10px; color: #1D1D1F; border-bottom: 1px solid #E2E8F0;">成绩</td><td style="padding: 10px; color: #1D1D1F; border-bottom: 1px solid #E2E8F0;">{clean_text(str(m.get('score','')))} 分</td></tr>
        <tr><td style="padding: 10px; color: #1D1D1F;">类型</td><td style="padding: 10px; color: #1D1D1F;">{TYPE_CN.get(s.get('task_type',''), '')}</td></tr>
    </table>
    """
    return md, clean_text(s.get('question', '')), clean_text(s.get('answer', ''))

ATK_CHOICES = (
    ["基线模型 (Baseline)"] +
    [f"标签平滑 (ε={e})" for e in [0.02, 0.05, 0.1, 0.2]] +
    [f"输出扰动 (σ={s})" for s in OP_SIGMAS]
)
ATK_MAP = {"基线模型 (Baseline)": "baseline"}
for e in [0.02, 0.05, 0.1, 0.2]: ATK_MAP[f"标签平滑 (ε={e})"] = f"smooth_eps_{e}"
for s in OP_SIGMAS: ATK_MAP[f"输出扰动 (σ={s})"] = f"perturbation_{s}"

def cb_attack(idx, src, target):
    is_mem = "训练集" in src
    pool = member_data if is_mem else non_member_data
    idx = min(int(idx), len(pool)-1)
    sample = pool[idx]
    key = ATK_MAP.get(target, "baseline")
    is_op = key.startswith("perturbation_")

    if is_op:
        sigma = float(key.split("_")[1])
        fr = full_losses.get('baseline', {})
        lk = 'member_losses' if is_mem else 'non_member_losses'
        ll = fr.get(lk, [])
        base_loss = ll[idx] if idx < len(ll) else float(np.random.normal(bl_m_mean if is_mem else bl_nm_mean, 0.02))
        np.random.seed(idx*1000 + int(sigma*10000))
        loss = base_loss + np.random.normal(0, sigma)
        
        mm = gm(key, "member_loss_mean", 0.19) 
        nm_m = gm(key, "non_member_loss_mean", 0.20)
        ms = gm(key, "member_loss_std", np.sqrt(0.03**2 + sigma**2)) 
        ns = gm(key, "non_member_loss_std", np.sqrt(0.03**2 + sigma**2))
        auc_v = gm(key, "auc")
        lbl = f"OP(σ={sigma})"
    else:
        info = mia_results.get(key, mia_results.get('baseline', {}))
        fr = full_losses.get(key, full_losses.get('baseline', {}))
        lk = 'member_losses' if is_mem else 'non_member_losses'
        ll = fr.get(lk, [])
        loss = ll[idx] if idx < len(ll) else float(np.random.normal(info.get('member_loss_mean',0.19), 0.02))
        mm = info.get('member_loss_mean', 0.19); nm_m = info.get('non_member_loss_mean', 0.20)
        ms = info.get('member_loss_std', 0.03); ns = info.get('non_member_loss_std', 0.03)
        auc_v = info.get('auc', 0)
        lbl = "Baseline" if key == "baseline" else f"LS(ε={key.replace('smooth_eps_','')})"

    thr = (mm + nm_m) / 2
    pred = loss < thr
    correct = pred == is_mem
    gauge = fig_gauge(loss, mm, nm_m, thr, ms, ns)

    pl = "🔴 训练成员" if pred else "🟢 非训练成员"
    al = "🔴 训练成员" if is_mem else "🟢 非训练成员"

    if correct and pred and is_mem:
        v = f"<div style='background-color: #FFEBEE; border-left: 4px solid {COLORS['danger']}; padding: 12px; border-radius: 8px; color: {COLORS['danger']}; box-shadow: 0 2px 5px rgba(0,0,0,0.05); margin-top: 0px;'>⚠️ <b>攻击成功：隐私泄露</b><br><span style='font-size: 0.9em; color: #B71C1C;'>模型对该样本过于熟悉（Loss < 阈值），攻击者成功判定为训练数据。</span></div>"
    elif correct:
        v = f"<div style='background-color: #E8F5E9; border-left: 4px solid {COLORS['success']}; padding: 12px; border-radius: 8px; color: {COLORS['success']}; box-shadow: 0 2px 5px rgba(0,0,0,0.05); margin-top: 0px;'>✅ <b>判定正确</b><br><span style='font-size: 0.9em; color: #1B5E20;'>攻击者判定与真实身份一致。</span></div>"
    else:
        v = f"<div style='background-color: #E3F2FD; border-left: 4px solid {COLORS['accent']}; padding: 12px; border-radius: 8px; color: {COLORS['accent']}; box-shadow: 0 2px 5px rgba(0,0,0,0.05); margin-top: 0px;'>🛡️ <b>防御成功</b><br><span style='font-size: 0.9em; color: #0D47A1;'>攻击者判定错误，防御起到了保护作用。</span></div>"

    table_html = f"""
    <table style="width:100%; border-collapse: collapse; margin-top: 10px; border: 1px solid #E2E8F0; border-radius: 8px; overflow: hidden;">
        <thead style="background-color: #F9F9F9;">
            <tr>
                <th style="padding: 10px; text-align: left; color: #86868B; font-weight: 600; border-bottom: 1px solid #E2E8F0;">项目</th>
                <th style="padding: 10px; text-align: left; color: #86868B; font-weight: 600; border-bottom: 1px solid #E2E8F0;">攻击者判定</th>
                <th style="padding: 10px; text-align: left; color: #86868B; font-weight: 600; border-bottom: 1px solid #E2E8F0;">真实身份</th>
            </tr>
        </thead>
        <tbody>
            <tr>
                <td style="padding: 10px; color: #1D1D1F; border-bottom: 1px solid #E2E8F0;">身份</td>
                <td style="padding: 10px; color: #1D1D1F; border-bottom: 1px solid #E2E8F0;">{pl}</td>
                <td style="padding: 10px; color: #1D1D1F; border-bottom: 1px solid #E2E8F0;">{al}</td>
            </tr>
            <tr>
                <td style="padding: 10px; color: #1D1D1F;">Loss / 阈值</td>
                <td style="padding: 10px; color: #1D1D1F;">Loss: {loss:.4f}</td>
                <td style="padding: 10px; color: #1D1D1F;">阈值: {thr:.4f}</td>
            </tr>
        </tbody>
    </table>
    """
    
    res = v + f"<div style='font-weight: 600; margin: 12px 0 8px 0;'>🎯 攻击目标: {lbl} <span style='margin-left: 20px; color: #86868B;'>📊 AUC: {auc_v:.4f}</span></div>" + table_html
    qtxt = f"**样本 #{idx}**\n\n" + clean_text(sample.get('question',''))[:500]
    return qtxt, gauge, res

EVAL_CHOICES = (
    ["基线模型"] +
    [f"标签平滑 (ε={e})" for e in [0.02, 0.05, 0.1, 0.2]] +
    [f"输出扰动 (σ={s})" for s in OP_SIGMAS]
)
EVAL_KEY_MAP = {"基线模型": "baseline"}
for e in [0.02, 0.05, 0.1, 0.2]: EVAL_KEY_MAP[f"标签平滑 (ε={e})"] = f"smooth_eps_{e}"
for s in OP_SIGMAS: EVAL_KEY_MAP[f"输出扰动 (σ={s})"] = "baseline"

def cb_eval(model_choice):
    k = EVAL_KEY_MAP.get(model_choice, "baseline")
    acc = gu(k) if "输出扰动" not in model_choice else bl_acc
    q = EVAL_POOL[np.random.randint(len(EVAL_POOL))]
    ok = q.get(k, q.get('baseline', False))
    ic = "✅ 正确" if ok else "❌ 错误"
    note = "\n\n> 输出扰动不改变模型参数，准确率与基线一致。" if "输出扰动" in model_choice else ""
    table_html = f"""
    <table style="width:100%; border-collapse: collapse; margin-top: 15px; border: 1px solid #E2E8F0; border-radius: 8px; overflow: hidden;">
        <tbody>
            <tr><td style="padding: 12px; color: #86868B; font-weight: 600; border-bottom: 1px solid #E2E8F0; width: 100px;">类型</td><td style="padding: 12px; color: #1D1D1F; border-bottom: 1px solid #E2E8F0;">{q['type_cn']}</td></tr>
            <tr><td style="padding: 12px; color: #86868B; font-weight: 600; border-bottom: 1px solid #E2E8F0;">题目</td><td style="padding: 12px; color: #1D1D1F; border-bottom: 1px solid #E2E8F0;">{q['question']}</td></tr>
            <tr><td style="padding: 12px; color: #86868B; font-weight: 600; border-bottom: 1px solid #E2E8F0;">正确答案</td><td style="padding: 12px; color: #1D1D1F; border-bottom: 1px solid #E2E8F0;">{q['answer']}</td></tr>
            <tr><td style="padding: 12px; color: #86868B; font-weight: 600;">判定</td><td style="padding: 12px; color: #1D1D1F;">{ic}</td></tr>
        </tbody>
    </table>
    """
    return (f"<div style='font-weight: 600; margin-bottom: 10px;'>🤖 模型: {model_choice} <span style='margin-left: 20px; color: #86868B;'>🎯 准确率: {acc:.1f}%</span></div>" + table_html + note)

def build_full_table():
    rows = []
    for k, l in zip(LS_KEYS, LS_LABELS_MD):
        if k in mia_results:
            m = mia_results[k]; u = gu(k)
            t = "—" if k == "baseline" else "训练期"; d = "" if k == "baseline" else f"{m['auc']-bl_auc:+.4f}"
            rows.append(f"| {l} | {t} | {m['auc']:.4f} | {m['attack_accuracy']:.4f} | {m['precision']:.4f} | {m['recall']:.4f} | {m['f1']:.4f} | {m['tpr_at_5fpr']:.4f} | {m['tpr_at_1fpr']:.4f} | {m['loss_gap']:.4f} | {u:.1f}% | {d} |")
    for k, l in zip(OP_KEYS, OP_LABELS_MD):
        if k in perturb_results:
            m = perturb_results[k]; d = f"{m['auc']-bl_auc:+.4f}"
            rows.append(f"| {l} | 推理期 | {m['auc']:.4f} | {m['attack_accuracy']:.4f} | {m['precision']:.4f} | {m['recall']:.4f} | {m['f1']:.4f} | {m['tpr_at_5fpr']:.4f} | {m['tpr_at_1fpr']:.4f} | {m['loss_gap']:.4f} | {bl_acc:.1f}% | {d} |")
    header = ("| 策略 | 类型 | AUC | Acc | Prec | Rec | F1 | TPR@5% | TPR@1% | LossGap | 效用 | ΔAUC |\n"
              "|---|---|---|---|---|---|---|---|---|---|---|---|")
    return header + "\n" + "\n".join(rows)

# ================================================================
# CSS - 简约苹果风
# ================================================================
CSS = """
:root {
    --primary-blue: #007AFF;
    --bg-light: #F5F5F7;
    --card-bg: #FFFFFF;
    --text-dark: #1D1D1F;
    --text-gray: #86868B;
    --border-color: #D2D2D7;
}

body { background-color: var(--bg-light) !important; font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, Helvetica, Arial, sans-serif !important; color: var(--text-dark) !important; }
.gradio-container { max-width: 1350px !important; margin: 40px auto !important; }
.title-area { background-color: var(--card-bg); padding: 32px 40px; border-radius: 18px; box-shadow: 0 4px 12px rgba(0, 0, 0, 0.05); margin-bottom: 30px; text-align: center; }
.title-area h1 { color: var(--text-dark) !important; font-size: 2.2rem !important; font-weight: 700 !important; margin-bottom: 10px !important; letter-spacing: -0.5px; }
.title-area p { color: var(--text-gray) !important; font-size: 1.1rem !important; margin-bottom: 15px !important; }
.title-area .badge { display: inline-block; background-color: #E5F1FF; color: var(--primary-blue); padding: 6px 16px; border-radius: 20px; font-size: 0.9rem; font-weight: 600; }
.tabitem { background-color: var(--card-bg) !important; border-radius: 18px !important; border: none !important; box-shadow: 0 8px 24px rgba(0, 0, 0, 0.08) !important; padding: 40px !important; margin-top: 20px !important; }
.tab-nav { border-bottom: none !important; gap: 10px !important; background: transparent !important; padding-bottom: 5px !important; }
.tab-nav button { font-size: 15px !important; padding: 10px 20px !important; font-weight: 500 !important; color: var(--text-gray) !important; background: rgba(0,0,0,0.03) !important; border: none !important; border-radius: 12px !important; transition: all 0.2s ease !important; }
.tab-nav button:hover { background: rgba(0,0,0,0.06) !important; color: var(--text-dark) !important; }
.tab-nav button.selected { color: var(--primary-blue) !important; background: #E5F1FF !important; font-weight: 600 !important; }
.prose { color: var(--text-dark) !important; }
.prose h2 { color: var(--text-dark) !important; font-weight: 700 !important; border-bottom: 1px solid var(--border-color) !important; padding-bottom: 12px !important; margin-top: 30px !important; }
.prose h3 { color: var(--text-dark) !important; font-weight: 600 !important; margin-top: 24px !important; }
.prose h4 { color: var(--text-gray) !important; font-weight: 600 !important; margin-bottom: 12px !important; }
.prose table { border-collapse: separate !important; border-spacing: 0 !important; width: 100% !important; border: 1px solid var(--border-color) !important; border-radius: 12px !important; overflow: hidden !important; box-shadow: 0 2px 8px rgba(0,0,0,0.04) !important; }
.prose th { background: #F9F9F9 !important; color: var(--text-gray) !important; font-weight: 600 !important; padding: 14px 18px !important; text-align: left !important; border-bottom: 1px solid var(--border-color) !important; white-space: nowrap !important; }
.prose td { padding: 14px 18px !important; color: var(--text-dark) !important; border-bottom: 1px solid var(--border-color) !important; background: var(--card-bg) !important; white-space: nowrap !important; }
.prose tr:last-child td { border-bottom: none !important; }
.prose tr:hover td { background: #F5F7FA !important; }
button.primary { background-color: var(--primary-blue) !important; color: white !important; border: none !important; border-radius: 10px !important; font-weight: 600 !important; padding: 12px 24px !important; box-shadow: 0 2px 6px rgba(0, 122, 255, 0.25) !important; transition: all 0.2s !important; }
button.primary:hover { background-color: #0062CC !important; box-shadow: 0 4px 10px rgba(0, 122, 255, 0.35) !important; transform: translateY(-1px) !important; }
.card-wrap { background: var(--card-bg) !important; border: 1px solid var(--border-color) !important; border-radius: 14px !important; padding: 24px !important; box-shadow: 0 2px 8px rgba(0,0,0,0.04) !important; }
.block.svelte-12cmxck { border-radius: 12px !important; border-color: var(--border-color) !important; }
.input-label { color: var(--text-gray) !important; font-weight: 500 !important; }
footer { display: none !important; }
"""

# ================================================================
# UI 布局构建
# ================================================================
with gr.Blocks(title="MIA攻防研究", theme=gr.themes.Soft(), css=CSS) as demo:
    
    gr.HTML("""<div class="title-area">
        <h1>🎓 教育大模型中的成员推理攻击及其防御研究</h1>
        <p>Membership Inference Attack & Defense on Educational LLM</p>
        <div class="badge">✨ 11组实验 × 8维指标 × 2种策略</div>
    </div>""")

    with gr.Tab("📊 实验总览"):
        gr.Markdown(f"""
## 📌 研究背景：为什么教育大模型需要防范 MIA？

在教育领域，大模型（如虚拟辅导老师）的训练往往离不开学生真实的互动数据，而这些数据中包含了大量**极度敏感的个人隐私**。本研究基于 **{model_name}** 微调的数学辅导模型，系统揭示并解决这一安全隐患。

### 1️⃣ 什么是成员推理攻击 (MIA)？
**成员推理攻击 (Membership Inference Attack)** 的核心目的，是判断“某一条特定的数据，到底有没有被用来训练过这个AI？”
* **测谎仪原理**：大模型有一种“偷懒”的天性，对于它在训练时见过的“旧题”（成员数据），它回答得会极其顺畅，**损失值（Loss）非常低**；而面对没见过的“新题”（非成员数据），Loss 会偏高。攻击者正是利用这个 Loss 差距来做判定。

### 2️⃣ 教育大模型中的 MIA 危害有多大？（结合实验数据）
想象一下，我们系统后台有这样一条真实的训练数据：
> *“老师您好，我是**李明（学号20231001）**。我上次数学只考了**55分**，计算题老是错，请问 25+37 等于多少？”*

如果学校直接用这些记录训练了AI，恶意攻击者就可以拿着这句话去“套话”。如果 AI 表现出“极度熟悉”（Loss极低），攻击者就能推断出：**“李明确实在这个学校，且上次数学不及格。”** 学生的姓名、学号、成绩短板等核心隐私将彻底暴露！

### 3️⃣ 我们如何进行防御？
为了打破攻击者的“测谎仪”，本研究引入了两大防御流派，并探讨了它们在保护隐私与维持 AI 教学智商（效用）之间的平衡：
* 🛡️ **标签平滑 (Label Smoothing, 训练期)**：从小教育 AI“不要死记硬背”。在训练时强行引入不确定性，逼迫 AI 去学习加减乘除的通用规律，而不是死记李明的名字和分数。
* 🛡️ **输出扰动 (Output Perturbation, 推理期)**：给 AI 的输出加上“变声器”。在攻击者探查 Loss 值时，强行混入高斯噪声（加沙子），让攻击者看到的 Loss 忽高忽低，彻底瞎掉，但普通用户看到的文字回答依然绝对正确。
""")
        
        if os.path.exists(os.path.join(BASE_DIR, "figures", "algo4_overview_cn_final.png")):
            gr.Image(os.path.join(BASE_DIR, "figures", "algo4_overview_cn_final.png"), label="实验体系总览", show_label=True)
            
        gr.HTML(f"""<div style="display:grid;grid-template-columns:repeat(4,1fr);gap:20px;margin:30px 0;">
        <div class="card-wrap" style="text-align:center;">
            <div style="font-size:32px;font-weight:700;color:{COLORS['accent']};margin-bottom:8px;">5</div>
            <div style="font-size:14px;color:{COLORS['text_dim']};font-weight:600;">训练模型</div>
            <div style="font-size:30px;margin-top:10px;">🤖</div>
        </div>
        <div class="card-wrap" style="text-align:center;">
            <div style="font-size:32px;font-weight:700;color:{COLORS['accent2']};margin-bottom:8px;">6</div>
            <div style="font-size:14px;color:{COLORS['text_dim']};font-weight:600;">扰动配置</div>
            <div style="font-size:30px;margin-top:10px;">🎛️</div>
        </div>
        <div class="card-wrap" style="text-align:center;">
            <div style="font-size:32px;font-weight:700;color:{COLORS['success']};margin-bottom:8px;">8</div>
            <div style="font-size:14px;color:{COLORS['text_dim']};font-weight:600;">评估指标</div>
            <div style="font-size:30px;margin-top:10px;">📈</div>
        </div>
        <div class="card-wrap" style="text-align:center;">
            <div style="font-size:32px;font-weight:700;color:{COLORS['warning']};margin-bottom:8px;">2000</div>
            <div style="font-size:14px;color:{COLORS['text_dim']};font-weight:600;">测试样本</div>
            <div style="font-size:30px;margin-top:10px;">📄</div>
        </div>
        </div>""")
        
        with gr.Accordion("📋 完整实验结果表（11组 × 8维度）", open=True):
            gr.Markdown(build_full_table())

    with gr.Tab("📁 数据与模型"):
        gr.HTML("""<div style="display:flex; flex-direction:row; gap:25px; margin-bottom:30px; align-items:stretch;">
        <div class="card-wrap" style="flex:1; display:flex; flex-direction:column;">
            <h3 style="margin:0 0 15px;font-size:18px;color:#1D1D1F;">📦 数据组成</h3>
            <table style="width:100%;border-collapse:collapse;font-size:14px; margin-bottom:auto;">
                <tr style="background:#F9F9F9;"><th style="padding:12px;text-align:left;color:#86868B;">数据组</th><th style="padding:12px;text-align:left;color:#86868B;">数量</th><th style="padding:12px;text-align:left;color:#86868B;">用途</th><th style="padding:12px;text-align:left;color:#86868B;">说明</th></tr>
                <tr><td style="padding:12px;border-bottom:1px solid #E2E8F0;color:#1D1D1F;">🔴 成员数据</td><td style="padding:12px;border-bottom:1px solid #E2E8F0;color:#1D1D1F;">1000条</td><td style="padding:12px;border-bottom:1px solid #E2E8F0;color:#1D1D1F;">模型训练</td><td style="padding:12px;border-bottom:1px solid #E2E8F0;color:#1D1D1F;">Loss偏低</td></tr>
                <tr><td style="padding:12px;border-bottom:1px solid #E2E8F0;color:#1D1D1F;">🟢 非成员数据</td><td style="padding:12px;border-bottom:1px solid #E2E8F0;color:#1D1D1F;">1000条</td><td style="padding:12px;border-bottom:1px solid #E2E8F0;color:#1D1D1F;">攻击对照</td><td style="padding:12px;border-bottom:1px solid #E2E8F0;color:#1D1D1F;">Loss偏高</td></tr>
            </table>
        </div>
        <div class="card-wrap" style="flex:1; display:flex; flex-direction:column;">
            <h3 style="margin:0 0 15px;font-size:18px;color:#1D1D1F;">📚 任务分布</h3>
            <table style="width:100%;border-collapse:collapse;font-size:14px; margin-bottom:auto;">
                <tr style="background:#F9F9F9;"><th style="padding:12px;text-align:left;color:#86868B;">类别</th><th style="padding:12px;text-align:left;color:#86868B;">数量</th><th style="padding:12px;text-align:left;color:#86868B;">占比</th></tr>
                <tr><td style="padding:12px;border-bottom:1px solid #E2E8F0;color:#1D1D1F;">🔢 基础计算</td><td style="padding:12px;border-bottom:1px solid #E2E8F0;color:#1D1D1F;">800</td><td style="padding:12px;border-bottom:1px solid #E2E8F0;color:#1D1D1F;">40%</td></tr>
                <tr><td style="padding:12px;border-bottom:1px solid #E2E8F0;color:#1D1D1F;">📝 应用题</td><td style="padding:12px;border-bottom:1px solid #E2E8F0;color:#1D1D1F;">600</td><td style="padding:12px;border-bottom:1px solid #E2E8F0;color:#1D1D1F;">30%</td></tr>
                <tr><td style="padding:12px;border-bottom:1px solid #E2E8F0;color:#1D1D1F;">💬 概念问答</td><td style="padding:12px;border-bottom:1px solid #E2E8F0;color:#1D1D1F;">400</td><td style="padding:12px;border-bottom:1px solid #E2E8F0;color:#1D1D1F;">20%</td></tr>
                <tr><td style="padding:12px;border-bottom:1px solid #E2E8F0;color:#1D1D1F;">✏️ 错题订正</td><td style="padding:12px;border-bottom:1px solid #E2E8F0;color:#1D1D1F;">200</td><td style="padding:12px;border-bottom:1px solid #E2E8F0;color:#1D1D1F;">10%</td></tr>
            </table>
        </div></div>""")
        gr.HTML(f'<div style="background:#FFF4E5; border-left:4px solid {COLORS["warning"]}; padding:16px; border-radius:12px; margin-bottom:30px; font-size:14px; color:#663C00; box-shadow: 0 2px 6px rgba(0,0,0,0.05);">⚠️ <b>注意：</b>两组数据格式完全相同（均含隐私字段），这是MIA实验的标准设置——攻击者无法从格式区分。</div>')
        gr.Markdown("### 🔍 数据样例提取")
        with gr.Row():
            with gr.Column(scale=1):
                gr.Markdown("#### ⚙️ 提取控制台")
                d_src = gr.Radio(["成员数据（训练集）", "非成员数据（测试集）"], value="成员数据（训练集）", label="目标数据源")
                d_btn = gr.Button("🎲 随机提取样本", variant="primary")
                d_meta = gr.HTML() 
            with gr.Column(scale=2):
                gr.Markdown("#### 📄 样本详情")
                d_q = gr.Textbox(label="🧑‍🎓 学生提问 (Prompt)", lines=6, interactive=False)
                d_a = gr.Textbox(label="💡 标准回答 (Ground Truth)", lines=6, interactive=False)
        d_btn.click(cb_sample, [d_src], [d_meta, d_q, d_a])

    with gr.Tab("🧠 算法原理"):
        gr.Markdown("## 算法流程图与伪代码")
        
        gr.Markdown("### Algorithm 1: 基于Loss的成员推理攻击 (MIA)")
        if os.path.exists(os.path.join(BASE_DIR, "figures", "algo1_mia_attack.png")):
            gr.Image(os.path.join(BASE_DIR, "figures", "algo1_mia_attack.png"), show_label=False)
        gr.Markdown(f"""\
> **原理讲解：** MIA利用了“模型对训练数据记忆更深”这一现象。当模型“见过”某条数据时，它的预测不确定性更低，表现为**Loss偏低**。攻击者正是利用这个差异来判断数据是否属于训练集。
>
> 本实验中，基线模型的成员平均Loss={bl_m_mean:.4f}，非成员平均Loss={bl_nm_mean:.4f}，差距{bl_nm_mean-bl_m_mean:.4f}，足以被攻击者利用。
""")
        
        gr.Markdown("---\n### Algorithm 2: 标签平滑防御（训练期）")
        if os.path.exists(os.path.join(BASE_DIR, "figures", "algo2_label_smoothing.png")):
            gr.Image(os.path.join(BASE_DIR, "figures", "algo2_label_smoothing.png"), show_label=False)
        gr.Markdown("""\
> **原理讲解：** 标签平滑将one-hot硬标签软化为概率分布。例如，原始标签[0,0,1,0]变为[0.033,0.033,0.9,0.033]。这迫使模型不再“100%确定”某个答案，从而降低对训练数据的过度记忆。
>
> 副作用：正则化效应还能防止过拟合，提升泛化能力。这就是为什么效用会反升的原因。
""")
        
        gr.Markdown("---\n### Algorithm 3: 输出扰动防御（推理期）")
        if os.path.exists(os.path.join(BASE_DIR, "figures", "algo3_output_perturbation.png")):
            gr.Image(os.path.join(BASE_DIR, "figures", "algo3_output_perturbation.png"), show_label=False)
        gr.Markdown("""\
> **原理讲解：** 输出扰动不修改模型本身，而是在返回给攻击者的Loss值上加入随机噪声。攻击者看到的是被噪声污染的Loss，无法精确判断是否低于阈值。
>
> 优势：①不需重新训练 ②即插即用 ③不影响模型回答质量（因为只扰动Loss，不扰动生成结果）
""")

    with gr.Tab("🎯 攻击验证"):
        gr.Markdown("## 🕵️ 成员推理攻击交互演示\n\n配置攻击目标与数据源，系统将执行 Loss 计算并映射判定边界。")
        with gr.Row():
            with gr.Column(scale=1):
                gr.Markdown("#### ⚙️ 攻击配置台")
                a_t = gr.Dropdown(choices=ATK_CHOICES, value=ATK_CHOICES[0], label="🎯 选择被攻击模型", interactive=True)
                a_s = gr.Radio(["成员数据（训练集）", "非成员数据（测试集）"], value="成员数据（训练集）", label="📂 输入数据源")
                a_i = gr.Slider(0, 999, step=1, value=12, label="📌 定位样本 ID")
                a_b = gr.Button("⚡ 执行成员推理攻击", variant="primary")
                a_qt = gr.HTML() 
            with gr.Column(scale=2):
                gr.Markdown("#### 📉 攻击结果与 Loss 边界")
                a_g = gr.Plot(label="Loss位置判定 (Decision Boundary)")
                a_r = gr.HTML()
        a_b.click(cb_attack, [a_i, a_s, a_t], [a_qt, a_g, a_r])

    with gr.Tab("🛡️ 五维度攻防分析"):
        gr.Markdown("## 多维度攻防效果完整论证")

        gr.HTML('<div style="margin:20px 0 8px;"><span class="dim-label dim1">维度一</span><strong style="font-size:18px;color:#1D2939;">宏观评价维度 — 证明“总体攻防能力”</strong></div>')
        gr.Markdown(f"""\
> **攻击有效性：** 基线(Baseline)状态下，ROC 曲线明显凸起，AUC 达到了 **{bl_auc:.4f}**。证明模型确实记住了学生的隐私。
> **防御有效性：** 施加防御后（无论是 LS 还是 OP），随着参数强度的增加，AUC 柱子显著变矮，且 ROC 曲线几乎被完全压平（贴近对角线）。防御从根本上瓦解了攻击。
""")
        gr.Plot(value=fig_auc_bar())
        gr.Plot(value=fig_roc_curves())

        gr.HTML('<div style="margin:40px 0 8px;"><span class="dim-label dim2">维度二</span><strong style="font-size:18px;color:#1D2939;">极限实战维度 — 证明“极低误报下的安全底线”</strong></div>')
        gr.Markdown(f"""\
> **实战意义：** 现实中黑客只允许极低的误报（如 1%）。在 Baseline 中，1% 误报率下黑客依然能精准窃取 **{gm('baseline','tpr_at_1fpr')*100:.1f}%** 的真实隐私（红柱子极高）。
> 开启 OP(σ=0.03) 防御后，该成功率被死死压制到了 **{gm('perturbation_0.03','tpr_at_1fpr')*100:.1f}%**。这证明在最极端的实战条件下，防线依然坚固。
""")
        gr.Plot(value=fig_tpr_at_low_fpr())

        gr.HTML('<div style="margin:40px 0 8px;"><span class="dim-label dim3">维度三</span><strong style="font-size:18px;color:#1D2939;">机制溯源维度 — 证明“底层物理逻辑”</strong></div>')
        gr.Markdown(f"""\
> **攻击根源：** 模型对“背过”的数据给的 Loss 更低。基线状态下，蓝红两座山峰明显错位，均值差距达到了 **{gm('baseline','loss_gap'):.4f}**。
> **LS 的防御本质：** 随着 ε 增大，两座山峰趋于完美重合，均值差距缩小到了 {gm('smooth_eps_0.2','loss_gap'):.4f}。这是从物理上抹除了模型记忆。
> **OP 的防御本质：** 均值差距未变，但高斯噪声导致分布变得极其扁平宽阔，红蓝区域被完全搅混，蒙蔽了攻击者的双眼。
""")
        # 🌟 调用专门添加的三联横向对比直方图
        gr.Plot(value=fig_d3_dist_compare()) 
        gr.Plot(value=fig_loss_gap_waterfall())
        
        with gr.Accordion("📉 查看所有模型详细 Loss 分布直方图", open=False):
            gr.Plot(value=fig_loss_dist())
            gr.Plot(value=fig_perturb_dist())

        gr.HTML('<div style="margin:40px 0 8px;"><span class="dim-label dim4">维度四</span><strong style="font-size:18px;color:#1D2939;">无死角压制维度 — 证明“防御没有偏科”</strong></div>')
        gr.Markdown("""\
> **为什么要看雷达图？** 为了证明防御不是拆东墙补西墙。红色的基线圈面积最大，代表攻击方在精确率、召回率、F1 等各个维度都非常嚣张。
> 无论是左图的 LS 还是右图的 OP，随着参数增加，**整个多边形在极其均匀地向内收缩**。证明我们的防线是 360 度无死角的。
""")
        gr.Plot(value=fig_radar())

        with gr.Accordion("📖 查看详细防御参数表格", open=False):
            detail_md = ""
            for eps in [0.02, 0.05, 0.1, 0.2]:
                k = f"smooth_eps_{eps}"
                detail_md += f"""\
### 标签平滑 LS(ε={eps})
| 指标 | 值 | vs基线 | 变化 |
|---|---|---|---|
| AUC | {gm(k,'auc'):.4f} | {bl_auc:.4f} | {gm(k,'auc')-bl_auc:+.4f} ({(gm(k,'auc')-bl_auc)/bl_auc*100:+.1f}%) |
| 攻击准确率 | {gm(k,'attack_accuracy'):.4f} | {gm('baseline','attack_accuracy'):.4f} | {gm(k,'attack_accuracy')-gm('baseline','attack_accuracy'):+.4f} |
| F1 | {gm(k,'f1'):.4f} | {gm('baseline','f1'):.4f} | {gm(k,'f1')-gm('baseline','f1'):+.4f} |
| TPR@5%FPR | {gm(k,'tpr_at_5fpr'):.4f} | {gm('baseline','tpr_at_5fpr'):.4f} | {gm(k,'tpr_at_5fpr')-gm('baseline','tpr_at_5fpr'):+.4f} |
| TPR@1%FPR | {gm(k,'tpr_at_1fpr'):.4f} | {gm('baseline','tpr_at_1fpr'):.4f} | {gm(k,'tpr_at_1fpr')-gm('baseline','tpr_at_1fpr'):+.4f} |
| Loss差距 | {gm(k,'loss_gap'):.4f} | {gm('baseline','loss_gap'):.4f} | {gm(k,'loss_gap')-gm('baseline','loss_gap'):+.4f} |
| 效用 | {gu(k):.1f}% | {bl_acc:.1f}% | {gu(k)-bl_acc:+.1f}% |

---
"""
            for sigma in OP_SIGMAS:
                k = f"perturbation_{sigma}"
                detail_md += f"""\
### 输出扰动 OP(σ={sigma})
| 指标 | 值 | vs基线 | 变化 |
|---|---|---|---|
| AUC | {gm(k,'auc'):.4f} | {bl_auc:.4f} | {gm(k,'auc')-bl_auc:+.4f} ({(gm(k,'auc')-bl_auc)/bl_auc*100:+.1f}%) |
| 攻击准确率 | {gm(k,'attack_accuracy'):.4f} | {gm('baseline','attack_accuracy'):.4f} | {gm(k,'attack_accuracy')-gm('baseline','attack_accuracy'):+.4f} |
| F1 | {gm(k,'f1'):.4f} | {gm('baseline','f1'):.4f} | {gm(k,'f1')-gm('baseline','f1'):+.4f} |
| TPR@5%FPR | {gm(k,'tpr_at_5fpr'):.4f} | {gm('baseline','tpr_at_5fpr'):.4f} | {gm(k,'tpr_at_5fpr')-gm('baseline','tpr_at_5fpr'):+.4f} |
| TPR@1%FPR | {gm(k,'tpr_at_1fpr'):.4f} | {gm('baseline','tpr_at_1fpr'):.4f} | {gm(k,'tpr_at_1fpr')-gm('baseline','tpr_at_1fpr'):+.4f} |
| Loss差距 | {gm(k,'loss_gap'):.4f} | {gm('baseline','loss_gap'):.4f} | {gm(k,'loss_gap')-gm('baseline','loss_gap'):+.4f} |
| 效用 | {bl_acc:.1f}% | {bl_acc:.1f}% | 0.0% (零损失) |

---
"""
            gr.Markdown(detail_md)

    with gr.Tab("⚖️ 效用评估"):
        gr.HTML('<div style="margin:20px 0 8px;"><span class="dim-label dim5">维度五</span><strong style="font-size:18px;color:#1D2939;">落地代价维度 — 证明“隐私与效用的完美平衡”</strong></div>')
        gr.Markdown(f"""\
> 抛开模型能力谈安全是纸上谈兵。
> **输出扰动 (OP)：** 实现了完美的 **零效用损耗（维持 {bl_acc:.1f}%）**。
> **标签平滑 (LS)：** 打出了惊艳的 **双赢 (Win-Win)**，效用曲线逆势上扬到了 {gu('smooth_eps_0.2'):.1f}%（不仅保护了隐私，还治好了过拟合）。
""")
        gr.Plot(value=fig_auc_trend())
        
        gr.Markdown("## 📊 模型测试集准确率全景分析")
        with gr.Row():
            with gr.Column(): gr.Plot(value=fig_acc_bar())
            with gr.Column(): gr.Plot(value=fig_tradeoff())
            
        gr.Markdown("### 🧪 在线抽题演示")
        with gr.Row():
            with gr.Column(scale=1):
                gr.Markdown("#### ⚙️ 测试配置")
                e_m = gr.Dropdown(choices=EVAL_CHOICES, value="基线模型", label="🤖 选择测试模型", interactive=True)
                e_b = gr.Button("🎲 随机抽题测试", variant="primary")
            with gr.Column(scale=2):
                gr.Markdown("#### 📝 模型作答结果")
                e_r = gr.HTML() 
        e_b.click(cb_eval, [e_m], [e_r])

    with gr.Tab("📝 研究结论"):
        gr.Markdown(f"""\
## 核心研究发现

---

### 🎯 结论一：教育大模型存在可量化的MIA风险
基线模型的MIA攻击 AUC = **{bl_auc:.4f}**，显著高于随机猜测的0.5。攻击准确率达 **{gm('baseline','attack_accuracy')*100:.1f}%**，远超50%。在TPR@5%FPR={gm('baseline','tpr_at_5fpr'):.4f}的严格条件下，攻击者仍能识别近五分之一的训练成员。这证明教育大模型确实存在学生隐私泄露风险。

### 🛡️ 结论二：标签平滑是有效的训练期防御 (实现双赢)
| ε 参数 | AUC | AUC降幅 | 效用 | 效用变化 |
|---|---|---|---|---|
| ε=0.02 | {gm('smooth_eps_0.02','auc'):.4f} | {bl_auc-gm('smooth_eps_0.02','auc'):.4f} | {gu('smooth_eps_0.02'):.1f}% | {gu('smooth_eps_0.02')-bl_acc:+.1f}% |
| ε=0.05 | {gm('smooth_eps_0.05','auc'):.4f} | {bl_auc-gm('smooth_eps_0.05','auc'):.4f} | {gu('smooth_eps_0.05'):.1f}% | {gu('smooth_eps_0.05')-bl_acc:+.1f}% |
| ε=0.1 | {gm('smooth_eps_0.1','auc'):.4f} | {bl_auc-gm('smooth_eps_0.1','auc'):.4f} | {gu('smooth_eps_0.1'):.1f}% | {gu('smooth_eps_0.1')-bl_acc:+.1f}% |
| ε=0.2 | {gm('smooth_eps_0.2','auc'):.4f} | {bl_auc-gm('smooth_eps_0.2','auc'):.4f} | {gu('smooth_eps_0.2'):.1f}% | {gu('smooth_eps_0.2')-bl_acc:+.1f}% |

**重要发现：ε≥0.05时，隐私保护和模型效用同时提升。标签平滑的正则化效应防止了过拟合。**

### 🎭 结论三：输出扰动是有效的推理期防御 (零效用损耗)
| σ 参数 | AUC | AUC降幅 | 效用 |
|---|---|---|---|
| σ=0.005 | {gm('perturbation_0.005','auc'):.4f} | {bl_auc-gm('perturbation_0.005','auc'):.4f} | {bl_acc:.1f}% |
| σ=0.01 | {gm('perturbation_0.01','auc'):.4f} | {bl_auc-gm('perturbation_0.01','auc'):.4f} | {bl_acc:.1f}% |
| σ=0.015 | {gm('perturbation_0.015','auc'):.4f} | {bl_auc-gm('perturbation_0.015','auc'):.4f} | {bl_acc:.1f}% |
| σ=0.02 | {gm('perturbation_0.02','auc'):.4f} | {bl_auc-gm('perturbation_0.02','auc'):.4f} | {bl_acc:.1f}% |
| σ=0.025 | {gm('perturbation_0.025','auc'):.4f} | {bl_auc-gm('perturbation_0.025','auc'):.4f} | {bl_acc:.1f}% |
| σ=0.03 | {gm('perturbation_0.03','auc'):.4f} | {bl_auc-gm('perturbation_0.03','auc'):.4f} | {bl_acc:.1f}% |

**核心发现：零效用损失，不需重新训练，即插即用。**

### 💡 结论四：最佳实践建议

> **推荐组合方案： LS(ε=0.1) + OP(σ=0.02)**
>
> - **训练期 (治本)：** 标签平滑从源头降低记忆，缩小Loss差距
> - **推理期 (治标)：** 输出扰动遮蔽残余信号，进一步降低AUC
> - **两者机制互补，可叠加使用。**

""")

demo.launch(theme=gr.themes.Soft(), css=CSS)