Update app.py

app.py

@@ -1,6 +1,6 @@
 # ================================================================
-# 教育大模型MIA攻防研究 - Gradio演示系统 v6.1 Final (苹果风)
-# 整合了双雷达图 + 算法流程图 + 伪代码 + 详尽数据分析 + 完整结论
+# 教育大模型MIA攻防研究 - Gradio演示系统 v7.0 学术巅峰版
+# 彻底消灭普通 e/s，全量启用 LaTeX 原生数学斜体 $\epsilon$ 和 $\sigma$
 # ================================================================
 
 import os
@@ -31,7 +31,6 @@ def clean_text(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")
@@ -58,12 +57,11 @@ except FileNotFoundError:
     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()} 
-        # 模拟方差变大
         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 - 简约苹果风
+# 全局图表配置
 # ================================================================
 COLORS = {
     'bg': '#FFFFFF',           
@@ -80,8 +78,6 @@ COLORS = {
     'ls_colors': ['#A0C4FF', '#70A1FF', '#478EFF', '#007AFF'], 
     'op_colors': ['#98F5E1', '#6EE7B7', '#34D399', '#10B981', '#059669', '#047857'], 
 }
-
-# 图表宽度配置 (为了适配双雷达图)
 CHART_W = 14
 
 def apply_light_style(fig, ax_or_axes):
@@ -100,17 +96,15 @@ def apply_light_style(fig, ax_or_axes):
         ax.grid(True, color=COLORS['grid'], alpha=0.6, linestyle='-', linewidth=0.8)
         ax.set_axisbelow(True) 
 
-# ================================================================
-# 提取指标的辅助函数
-# ================================================================
+# 🌟🌟🌟 核心修改：专门为画图准备的 LaTeX 格式标签 🌟🌟🌟
 LS_KEYS = ["baseline", "smooth_eps_0.02", "smooth_eps_0.05", "smooth_eps_0.1", "smooth_eps_0.2"]
-LS_LABELS_EN = ["Baseline", "LS(e=0.02)", "LS(e=0.05)", "LS(e=0.1)", "LS(e=0.2)"]
-LS_LABELS_CN = ["基线", "LS(ε=0.02)", "LS(ε=0.05)", "LS(ε=0.1)", "LS(ε=0.2)"]
+LS_LABELS_PLOT = ["Baseline", r"LS($\epsilon$=0.02)", r"LS($\epsilon$=0.05)", r"LS($\epsilon$=0.1)", r"LS($\epsilon$=0.2)"]
+LS_LABELS_UI = ["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_EN = [f"OP(s={s})" for s in OP_SIGMAS]
-OP_LABELS_CN = [f"OP(σ={s})" for s in OP_SIGMAS]
+OP_LABELS_PLOT = [f"OP($\sigma$={s})" for s in OP_SIGMAS]
+OP_LABELS_UI = [f"OP(σ={s})" for s in OP_SIGMAS]
 
 ALL_KEYS = LS_KEYS + OP_KEYS
 
@@ -129,12 +123,8 @@ 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'}
+TYPE_CN = {'calculation': '基础计算', 'word_problem': '应用题', 'concept': '概念问答', 'error_correction': '错题订正'}
 
-# ================================================================
-# 效用评估题库 
-# ================================================================
 np.random.seed(777)
 EVAL_POOL = []
 _types = ['calculation']*120 + ['word_problem']*90 + ['concept']*60 + ['error_correction']*30
@@ -148,8 +138,7 @@ for _i in range(300):
         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))]
+        _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")]
@@ -164,49 +153,35 @@ for _i in range(300):
     EVAL_POOL.append(item)
 
 # ================================================================
-# 图表绘制函数
+# 图表绘制函数 (全部更换为 LaTeX 渲染)
 # ================================================================
 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'])
-    
+    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)
+    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_EN)):
+    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_EN)):
+    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)
@@ -214,78 +189,35 @@ def fig_auc_bar():
     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=10)
+    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')
-
-    # --- 左图: 5个标签平滑模型 ---
-    ls_cfgs = [
-        ("Baseline",     "baseline",         '#F04438'),
-        ("LS(e=0.02)",   "smooth_eps_0.02",  '#B2DDFF'),
-        ("LS(e=0.05)",   "smooth_eps_0.05",  '#84CAFF'),
-        ("LS(e=0.1)",    "smooth_eps_0.1",   '#2E90FA'),
-        ("LS(e=0.2)",    "smooth_eps_0.2",   '#7A5AF8'),
-    ]
-
-    # --- 右图: Baseline + 6个输出扰动 ---
-    op_cfgs = [
-        ("Baseline",     "baseline",            '#F04438'),
-        ("OP(s=0.005)",  "perturbation_0.005",  '#A6F4C5'),
-        ("OP(s=0.01)",   "perturbation_0.01",   '#6CE9A6'),
-        ("OP(s=0.015)",  "perturbation_0.015",  '#32D583'),
-        ("OP(s=0.02)",   "perturbation_0.02",   '#12B76A'),
-        ("OP(s=0.025)",  "perturbation_0.025",  '#039855'),
-        ("OP(s=0.03)",   "perturbation_0.03",   '#027A48'),
-    ]
-
-    for ax_idx, (ax, cfgs, title) in enumerate([
-        (axes[0], ls_cfgs, 'Label Smoothing (5 models)'),
-        (axes[1], op_cfgs, 'Output Perturbation (7 configs)')
-    ]):
-        ax.set_facecolor('white')
+    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')
 
-        # 计算归一化用的最大值(基于当前子图的配置)
-        mx = []
-        for i, m_key in enumerate(mk):
-            val_max = max(gm(k, m_key) for _, k, _ in cfgs)
-            mx.append(val_max if val_max > 0 else 1)
+    ls_cfgs = [("Baseline", "baseline", '#F04438'), (r"LS($\epsilon$=0.02)", "smooth_eps_0.02", '#B2DDFF'), (r"LS($\epsilon$=0.05)", "smooth_eps_0.05", '#84CAFF'), (r"LS($\epsilon$=0.1)", "smooth_eps_0.1", '#2E90FA'), (r"LS($\epsilon$=0.2)", "smooth_eps_0.2", '#7A5AF8')]
+    op_cfgs = [("Baseline", "baseline", '#F04438'), (r"OP($\sigma$=0.005)", "perturbation_0.005", '#A6F4C5'), (r"OP($\sigma$=0.01)", "perturbation_0.01", '#6CE9A6'), (r"OP($\sigma$=0.015)", "perturbation_0.015", '#32D583'), (r"OP($\sigma$=0.02)", "perturbation_0.02", '#12B76A'), (r"OP($\sigma$=0.025)", "perturbation_0.025", '#039855'), (r"OP($\sigma$=0.03)", "perturbation_0.03", '#027A48')]
 
+    for ax_idx, (ax, cfgs, title) in enumerate([(axes[0], ls_cfgs, 'Label Smoothing (5 models)'), (axes[1], op_cfgs, 'Output Perturbation (7 configs)')]):
+        ax.set_facecolor('white')
+        mx = [max(gm(k, m_key) for _, k, _ in cfgs) for m_key in mk]; mx = [m if m > 0 else 1 for m in mx]
         for nm, ky, cl in cfgs:
-            v = [gm(ky, m_key) / mx[i] for i, m_key in enumerate(mk)]
-            v += [v[0]]  # 闭合
-            lw = 2.8 if ky == 'baseline' else 1.8
-            alpha_fill = 0.10 if ky == 'baseline' else 0.04
-            ax.plot(ag, v, 'o-', lw=lw, label=nm, color=cl, ms=5,
-                    alpha=0.95 if ky == 'baseline' else 0.85)
-            ax.fill(ag, v, alpha=alpha_fill, color=cl)
-
-        ax.set_xticks(ag[:-1])
-        ax.set_xticklabels(ms, fontsize=9, color=COLORS['text'])
-        ax.set_yticklabels([])
-        ax.set_title(title, fontsize=11, fontweight='700',
-                     color=COLORS['text'], pad=18)
-        ax.legend(loc='upper right',
-                  bbox_to_anchor=(1.35 if ax_idx == 1 else 1.30, 1.12),
-                  fontsize=8, framealpha=0.9, edgecolor=COLORS['grid'])
-        ax.spines['polar'].set_color(COLORS['grid'])
-        ax.grid(color=COLORS['grid'], alpha=0.5)
-
+            v = [gm(ky, m_key) / mx[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=12, fontweight='700', color=COLORS['text'], pad=18)
+        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
 
 def fig_loss_dist():
-    items = [(k, l, gm(k, 'auc')) for k, l in zip(LS_KEYS, LS_LABELS_EN) if k in full_losses]; n = len(items)
+    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):
@@ -307,14 +239,14 @@ def fig_perturb_dist():
         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(s={s})\nAUC={pa:.4f}', fontsize=11, fontweight='semibold'); ax.set_xlabel('Loss', fontsize=10)
+        ax.set_title(f'OP($\sigma$={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_EN)):
+    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])
@@ -327,56 +259,58 @@ def fig_roc_curves():
         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(s={s}) (AUC={auc_p:.4f})')
+            ax.plot(fpr_p, tpr_p, color=COLORS['op_colors'][i], lw=2, label=f'OP($\sigma$={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_EN)): 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_EN)): 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])
+    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=10); 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.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=10); 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()
+    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_acc_bar():
     names, vals, clrs = [], [], []; ls_c = [COLORS['baseline']] + COLORS['ls_colors']
-    for i, (k, l) in enumerate(zip(LS_KEYS, LS_LABELS_EN)):
+    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_EN)):
+    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=(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+1, f'{v:.1f}%', ha='center', fontsize=10, fontweight='semibold', color=COLORS['text'])
-    ax.set_ylabel('Test Accuracy (%)', fontsize=12, fontweight='medium'); ax.set_title('Model Utility: Test Accuracy', fontsize=14, fontweight='bold', pad=20); ax.set_ylim(0, 105); ax.set_xticks(range(len(names))); ax.set_xticklabels(names, rotation=30, ha='right', fontsize=10); plt.tight_layout()
+    ax.set_ylabel('Test Accuracy (%)', fontsize=12, fontweight='medium'); ax.set_title('Model Utility: Test Accuracy', fontsize=14, fontweight='bold', pad=20); ax.set_ylim(0, 105); ax.set_xticks(range(len(names))); ax.set_xticklabels(names, rotation=30, ha='right', fontsize=11); plt.tight_layout()
     return fig
 
 def fig_tradeoff():
     fig, ax = plt.subplots(figsize=(11, 8)); 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_EN)):
+    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=180, edgecolors='white', lw=1.5, zorder=5, alpha=0.9)
     op_markers = ['^', 'D', 'v', 'P', 'X', 'h']
-    for i, (k, l) in enumerate(zip(OP_KEYS, OP_LABELS_EN)):
+    for i, (k, l) in enumerate(zip(OP_KEYS, OP_LABELS_PLOT)):
         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=180, edgecolors='white', lw=1.5, zorder=5, alpha=0.9)
     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=14, fontweight='bold', pad=20); ax.legend(fontsize=10, loc='upper left', ncol=2, facecolor=COLORS['bg'], edgecolor='none', labelcolor=COLORS['text']); plt.tight_layout()
     return fig
 
 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 (left)', 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.set_xlabel('Label Smoothing epsilon', 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]; ax.legend(lines, labels, fontsize=10, facecolor=COLORS['bg'], edgecolor='none', labelcolor=COLORS['text'])
-    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'); ax.set_xlabel('Perturbation Sigma', 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); ax.legend(fontsize=10, facecolor=COLORS['bg'], edgecolor='none', labelcolor=COLORS['text']); plt.tight_layout()
+    ax2 = ax.twinx(); line1 = ax.plot(eps_vals, auc_vals, 'o-', color=COLORS['danger'], lw=3, ms=9, label='MIA AUC (left)', 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.set_xlabel(r'Label Smoothing $\epsilon$', 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]; ax.legend(lines, labels, fontsize=10, facecolor=COLORS['bg'], edgecolor='none', labelcolor=COLORS['text'])
+    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')
+    ax.set_xlabel(r'Perturbation $\sigma$', 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); ax.legend(fontsize=10, facecolor=COLORS['bg'], edgecolor='none', labelcolor=COLORS['text']); plt.tight_layout()
     return fig
 
 def fig_loss_gap_waterfall():
     fig, ax = plt.subplots(figsize=(14, 6.5)); 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_EN)): 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_EN)): names.append(l); gaps.append(gm(k, 'loss_gap')); clrs.append(COLORS['op_colors'][i])
+    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=10); 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()
+    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
 
 # ================================================================
@@ -401,6 +335,7 @@ def cb_sample(src):
     """
     return md, clean_text(s.get('question', '')), clean_text(s.get('answer', ''))
 
+# 🌟 下拉框选项也全部替换为 ε 和 σ
 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]] +
@@ -517,12 +452,12 @@ def cb_eval(model_choice):
 
 def build_full_table():
     rows = []
-    for k, l in zip(LS_KEYS, LS_LABELS_CN):
+    for k, l in zip(LS_KEYS, LS_LABELS_UI):
         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_CN):
+    for k, l in zip(OP_KEYS, OP_LABELS_UI):
         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} |")
@@ -605,8 +540,8 @@ with gr.Blocks(title="MIA攻防研究") as demo:
 """)
         
         # 实验体系总览图 (如果在目录里则显示)
-        if os.path.exists(os.path.join(BASE_DIR, "figures", "algo4_overview.png")):
-            gr.Image(os.path.join(BASE_DIR, "figures", "algo4_overview.png"), label="实验体系总览", show_label=True)
+        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;">
@@ -719,24 +654,22 @@ with gr.Blocks(title="MIA攻防研究") as demo:
         gr.Markdown(f"### 1️⃣ 攻击成功率全景对比 (AUC)\n\n> 柱子越短 = AUC越低 = 防御越有效。基线AUC={bl_auc:.4f}，标签平滑最低降至{gm('smooth_eps_0.2','auc'):.4f}，输出扰动最低降至{gm('perturbation_0.03','auc'):.4f}。")
         gr.Plot(value=fig_auc_bar())
         
-        # --- 整合双雷达图及配套讲解文本 ---
         gr.Markdown(f"""\
 ### 2️⃣ 多指标雷达图对比（全部11组实验）
 
 > **左图：标签平滑系列5个模型**
 > - 红色(Baseline)面积最大 = 攻击全面有效
-> - 随着ε从0.02−0.2增大，雷达面积逐步缩小 = 防御逐步增强
+> - 随着 ε 从 0.02 增至 0.2，雷达面积逐步缩小 = 防御逐步增强
 > - 特别注意 TPR@1%FPR 和 LossGap 两个轴，缩小最显著
 >
 > **右图：输出扰动系列7个配置**
 > - 红色(Baseline)同样是最大的
-> - 随着σ从0.005−0.03增大，绿色系雷达逐步缩小
+> - 随着 σ 从 0.005 增至 0.03，绿色系雷达逐步缩小
 > - OP在LossGap和TPR@5%维度上降幅尤其明显
 >
 > **结论：** 两种防御均在所有维度上全面压制攻击能力，不是只降低了某一个指标。
 """)
         gr.Plot(value=fig_radar())
-        # ---------------------------------
         
         gr.Markdown("### 3️⃣ ROC曲线对比\n\n> 曲线越贴近对角线=攻击越接近随机猜测=防御越有效。左图标签平滑，右图输出扰动。")
         gr.Plot(value=fig_roc_curves())
@@ -862,4 +795,4 @@ with gr.Blocks(title="MIA攻防研究") as demo:
 
 """)
 
-demo.launch(theme=gr.themes.Soft(), css=CSS)
+demo.launch()