"""plot_robustness.py — visualize robustness sweep results. Reads robustnessv3/runs.json and produces: - robustness_overall.png : 4 metrics x 7 perturbations, line plot per metric - robustness_per_fake.png: AUROC per (fake-model x perturbation), 1 row per fake - robustness_table.csv : flat CSV (perturbation, level, param, metrics) Run: /opt/conda/envs/LipFD/bin/python plot_robustness.py \ --runs robustnessv3/runs.json --out_dir robustnessv3 """ import argparse import csv as _csv import json import os import matplotlib.pyplot as plt import numpy as np # Same order as evaluate_robustness.py SEVERITY dict PERTURBATIONS = ["color_saturation", "color_contrast", "block_wise", "gaussian_noise", "gaussian_blur", "pixelate", "jpeg_quality"] # Visual styling — distinct color per perturbation, consistent across plots COLORS = { "color_saturation": "#1f77b4", "color_contrast": "#ff7f0e", "block_wise": "#2ca02c", "gaussian_noise": "#d62728", "gaussian_blur": "#9467bd", "pixelate": "#8c564b", "jpeg_quality": "#e377c2", } MARKERS = { "color_saturation": "o", "color_contrast": "s", "block_wise": "^", "gaussian_noise": "D", "gaussian_blur": "v", "pixelate": "P", "jpeg_quality": "X", } def load_runs(path): with open(path) as f: return json.load(f)["runs"] def organize(runs): """{perturbation: {level: run_dict}} — level 1 baseline copied to every perturbation.""" out = {p: {} for p in PERTURBATIONS} baseline = None for r in runs: if r["level"] == 1: baseline = r break for r in runs: out[r["perturbation"]][r["level"]] = r if baseline is not None: for p in PERTURBATIONS: out[p][1] = baseline return out, baseline def write_csv(runs, csv_path): rows = [] for r in runs: o = r["overall_clip"] rows.append({ "perturbation": r["perturbation"], "level": r["level"], "param": r["param"], "n_clips": r["n_clips"], "AUROC": o["AUROC"], "AP": o["AP"], "Accuracy": o["Accuracy"], "Acc@EER": o["Acc@EER"], "TPR@FPR=1%": o["TPR@FPR=1%"], "TPR@FPR=0.1%": o["TPR@FPR=0.1%"], }) rows.sort(key=lambda x: (x["perturbation"], x["level"])) with open(csv_path, "w", newline="") as f: w = _csv.DictWriter(f, fieldnames=list(rows[0].keys())) w.writeheader() w.writerows(rows) print(f" wrote {csv_path} ({len(rows)} rows)") def plot_overall(by_pert, out_path, baseline): """4 panels: AUROC / Accuracy / Acc@EER / TPR@FPR=1%, level on X axis.""" metrics = [ ("AUROC", "AUROC"), ("Accuracy", "Accuracy"), ("Acc@EER", "Acc@EER"), ("TPR@FPR=1%", "TPR@FPR=1%"), ] fig, axes = plt.subplots(2, 2, figsize=(13, 9)) axes = axes.flatten() levels = [1, 2, 3, 4, 5] for ax, (key, title) in zip(axes, metrics): for p in PERTURBATIONS: ys = [] for L in levels: r = by_pert[p].get(L) if r is None: ys.append(np.nan) else: ys.append(r["overall_clip"][key]) ax.plot(levels, ys, marker=MARKERS[p], color=COLORS[p], label=p, linewidth=1.8, markersize=7) if baseline is not None: bl = baseline["overall_clip"][key] ax.axhline(bl, color="grey", linestyle="--", alpha=0.5, linewidth=1, label=f"clean baseline = {bl:.4f}") ax.set_title(title, fontsize=12) ax.set_xlabel("perturbation level (1=clean, 5=heaviest)") ax.set_ylabel(title) ax.set_xticks(levels) ax.grid(alpha=0.3) # one shared legend on top-right axis handles, labels = axes[0].get_legend_handles_labels() fig.legend(handles, labels, loc="lower center", ncol=4, fontsize=9, frameon=False, bbox_to_anchor=(0.5, -0.02)) fig.suptitle("LipFD robustness — overall (clip-level), epoch_44 ckpt", fontsize=14) plt.tight_layout(rect=[0, 0.04, 1, 0.97]) plt.savefig(out_path, dpi=140, bbox_inches="tight") plt.close() print(f" wrote {out_path}") def plot_per_fake(by_pert, out_path, baseline): """1 row per fake model (EDTalk / Float / SadTalk), each row = AUROC vs level for every perturbation.""" fakes = sorted(set(baseline["per_fake_vs_real"].keys())) fig, axes = plt.subplots(1, len(fakes), figsize=(5 * len(fakes), 4.5), sharey=True) if len(fakes) == 1: axes = [axes] levels = [1, 2, 3, 4, 5] for ax, fm in zip(axes, fakes): for p in PERTURBATIONS: ys = [] for L in levels: r = by_pert[p].get(L) ys.append(r["per_fake_vs_real"][fm]["AUROC"] if r else np.nan) ax.plot(levels, ys, marker=MARKERS[p], color=COLORS[p], label=p, linewidth=1.6, markersize=6) if baseline is not None: bl = baseline["per_fake_vs_real"][fm]["AUROC"] ax.axhline(bl, color="grey", linestyle="--", alpha=0.5, linewidth=1) ax.set_title(f"{fm} + Real (AUROC)") ax.set_xlabel("level") ax.set_xticks(levels) ax.grid(alpha=0.3) axes[0].set_ylabel("AUROC") handles, labels = axes[0].get_legend_handles_labels() fig.legend(handles, labels, loc="lower center", ncol=4, fontsize=9, frameon=False, bbox_to_anchor=(0.5, -0.04)) fig.suptitle("LipFD robustness — per-fake AUROC, epoch_44 ckpt", fontsize=14) plt.tight_layout(rect=[0, 0.06, 1, 0.95]) plt.savefig(out_path, dpi=140, bbox_inches="tight") plt.close() print(f" wrote {out_path}") def plot_fairness(by_pert, out_path): """3 panels (gender/race4/age_group), each shows F_MEO trend per perturbation.""" dims = ["gender", "race4", "age_group"] fig, axes = plt.subplots(1, 3, figsize=(15, 4.5)) levels = [1, 2, 3, 4, 5] for ax, d in zip(axes, dims): for p in PERTURBATIONS: ys = [] for L in levels: r = by_pert[p].get(L) fb = r["fairness_overall"].get(d) if r else None ys.append(fb["F_MEO"] if fb else np.nan) ax.plot(levels, ys, marker=MARKERS[p], color=COLORS[p], label=p, linewidth=1.6, markersize=6) ax.set_title(f"F_MEO ({d}) — lower is fairer") ax.set_xlabel("level") ax.set_xticks(levels) ax.grid(alpha=0.3) axes[0].set_ylabel("F_MEO (%)") handles, labels = axes[0].get_legend_handles_labels() fig.legend(handles, labels, loc="lower center", ncol=4, fontsize=9, frameon=False, bbox_to_anchor=(0.5, -0.04)) fig.suptitle("LipFD robustness — fairness F_MEO across perturbations", fontsize=14) plt.tight_layout(rect=[0, 0.06, 1, 0.95]) plt.savefig(out_path, dpi=140, bbox_inches="tight") plt.close() print(f" wrote {out_path}") def parse_args(): p = argparse.ArgumentParser() p.add_argument("--runs", required=True) p.add_argument("--out_dir", required=True) return p.parse_args() def main(): args = parse_args() runs = load_runs(args.runs) by_pert, baseline = organize(runs) os.makedirs(args.out_dir, exist_ok=True) write_csv(runs, os.path.join(args.out_dir, "robustness_table.csv")) plot_overall(by_pert, os.path.join(args.out_dir, "robustness_overall.png"), baseline) plot_per_fake(by_pert, os.path.join(args.out_dir, "robustness_per_fake.png"), baseline) plot_fairness(by_pert, os.path.join(args.out_dir, "robustness_fairness.png")) if __name__ == "__main__": main()