Update app.py

app.py

@@ -1,6 +1,6 @@
 # ================================================================
-# 教育大模型MIA攻防研究 - Gradio演示系统 v6.0 Final (苹果风)
-# 整合了算法流程图 + 伪代码 + 详尽数据分析 + 完整结论 + 完美UI修复
+# 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
 # ================================================================
 
 import os
@@ -81,6 +81,9 @@ COLORS = {
     '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):
     fig.patch.set_facecolor(COLORS['bg'])
     axes = ax_or_axes if hasattr(ax_or_axes, '__iter__') else [ax_or_axes]
@@ -215,20 +218,70 @@ def fig_auc_bar():
     ax.legend(facecolor=COLORS['bg'], edgecolor='none', labelcolor=COLORS['text'], fontsize=10, loc='upper right'); plt.tight_layout()
     return fig
 
-def fig_radar_compare():
-    metrics = ['AUC', 'Attack Acc', 'Precision', 'Recall', 'F1', 'TPR@5%', 'TPR@1%', 'LossGap']
-    metric_keys = ['auc', 'attack_accuracy', 'precision', 'recall', 'f1', 'tpr_at_5fpr', 'tpr_at_1fpr', 'loss_gap']
-    configs = [("Baseline", "baseline", COLORS['danger']),("LS(e=0.1)", "smooth_eps_0.1", COLORS['accent']),("LS(e=0.2)", "smooth_eps_0.2", COLORS['accent2']),("OP(s=0.02)", "perturbation_0.02", COLORS['success'])]
-    N = len(metrics); angles = np.linspace(0, 2*np.pi, N, endpoint=False).tolist(); angles += angles[:1]
-    fig, ax = plt.subplots(figsize=(8, 8), subplot_kw=dict(polar=True)); fig.patch.set_facecolor(COLORS['bg']); ax.set_facecolor(COLORS['panel'])
-    maxes = [max(gm(k, mk) for _, k, _ in configs) for mk in metric_keys]; maxes = [m if m > 0 else 1 for m in maxes]
-    for name, key, color in configs:
-        vals = [gm(key, mk)/maxes[i] for i, mk in enumerate(metric_keys)]; vals += vals[:1]
-        ax.plot(angles, vals, 'o-', linewidth=2.5, label=name, color=color, markersize=7); ax.fill(angles, vals, alpha=0.15, color=color)
-    ax.set_xticks(angles[:-1]); ax.set_xticklabels(metrics, fontsize=11, color=COLORS['text'], fontweight='medium'); ax.set_yticklabels([])
-    ax.set_title('Multi-Metric Radar: Attack vs Defense', fontsize=14, fontweight='bold', color=COLORS['text'], pad=30)
-    ax.legend(loc='upper right', bbox_to_anchor=(1.35, 1.1), facecolor=COLORS['bg'], edgecolor='none', labelcolor=COLORS['text'], fontsize=10)
-    ax.spines['polar'].set_color(COLORS['grid']); ax.tick_params(axis='y', colors=COLORS['grid']); ax.grid(color=COLORS['grid'], alpha=0.5); plt.tight_layout()
+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')
+
+        # 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)
+
+        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)
+
+    plt.tight_layout()
     return fig
 
 def fig_loss_dist():
@@ -615,7 +668,6 @@ with gr.Blocks(title="MIA攻防研究") as demo:
                 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])
 
-    # ===== 新增：算法原理 Tab =====
     with gr.Tab("🧠 算法原理"):
         gr.Markdown("## 算法流程图与伪代码")
         
@@ -666,8 +718,26 @@ with gr.Blocks(title="MIA攻防研究") as demo:
         gr.Markdown("## 🔍 多维度攻防效果对比分析")
         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())
-        gr.Markdown("### 2️⃣ 多指标雷达图对比\n\n> 红色区域(基线)越大=攻击越强，青/绿色区域(防御)越小=防御越有效。防御后所有维度均有显著缩小。")
-        gr.Plot(value=fig_radar_compare())
+        
+        # --- 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
+> - 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
+> - 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())
         gr.Markdown(f"### 4️⃣ 低误报率下的攻击能力\n\n> 基线 TPR@5%FPR={gm('baseline','tpr_at_5fpr'):.4f}，防御后显著下降。这是衡量攻击危害的最严格指标。")
@@ -682,7 +752,6 @@ with gr.Blocks(title="MIA攻防研究") as demo:
         with gr.Accordion("📉 Loss分布直方图（输出扰动 6组）", open=False):
             gr.Plot(value=fig_perturb_dist())
             
-        # 每个模型详尽分析
         with gr.Accordion("📖 每个模型/参数详细分析", open=False):
             detail_md = "## 逐一详细分析\n\n"
             detail_md += f"""\
@@ -751,7 +820,6 @@ with gr.Blocks(title="MIA攻防研究") as demo:
         e_b.click(cb_eval, [e_m], [e_r])
 
     with gr.Tab("📝 研究结论"):
-        # 融入了新提供的详细结论
         gr.Markdown(f"""\
 ## 核心研究发现
 
@@ -794,4 +862,4 @@ with gr.Blocks(title="MIA攻防研究") as demo:
 
 """)
 
-demo.launch(theme=gr.themes.Soft(), css=CSS) 
+demo.launch(theme=gr.themes.Soft(), css=CSS)