Implement ATT attack with gradient variance reduction

.gitignore

@@ -1,4 +1,6 @@
+venv/
 __pycache__/
 *.pyc
-*.pyo
-.DS_Store
+dataset_extractedfaces.zip
+venv/
+__pycache__/

READMEforAI.txt

@@ -0,0 +1,75 @@
+Repo orientation for coding agents
+
+Purpose
+- This repository is a standalone vanilla transfer-attack exercise for interns.
+- It is not the FaceSM repo and should not be treated as a paper-reproduction branch.
+- The goal is to add a new transfer attack and compare it against the existing vanilla baselines.
+
+What is already implemented
+- Baseline attacks in core/transfer_attack_core.py:
+  - PGD
+  - MI_FGSM
+  - TI_FGSM
+  - SI_NI_FGSM
+  - MI_ADMIX_DI_TI
+- Supported attacker models:
+  - Facenet512
+  - ArcFace
+  - GhostFaceNet
+  - VGG-Face
+- Victim pool used for evaluation:
+  - FaceNet
+  - FaceNet512
+  - ArcFace
+  - GhostFaceNet
+  - VGG-Face
+  - IR152
+- IR152 resources should be taken from the shared Drive folder, not from this repo.
+
+Important exclusions
+- Do not add FaceSM logic here.
+- Do not add old RAP code from another branch.
+- Do not add API-specific code paths.
+- Keep this repo focused on vanilla baselines plus a newly implemented attack.
+
+Key files
+- README.md
+  - High-level description of the assignment repo.
+- docs/trackA_assignment.md
+  - Main assignment instructions for interns.
+- docs/subset_input_pairs.csv
+  - Small subset that interns should use first.
+- results_baseline/subset_attack_summary.csv
+  - Current baseline leaderboard on the subset.
+- results_baseline/subset_attack_summary_by_goal.csv
+  - Baseline comparison split into impersonation and dodging.
+- results_baseline/subset_attacker_victim_summary.csv
+  - Baseline attacker-victim breakdown.
+- results_baseline/subset_attack_eval_long.csv
+  - Long-format evaluated baseline table.
+- results_baseline/subset_raw_similarities_long.csv
+  - Raw similarity values used to derive the subset baseline summaries.
+- scripts/build_subset_baselines.py
+  - Rebuilds subset baseline CSVs from the raw long similarity file.
+- experiments/run_vanilla_subset_generation.py
+  - Minimal baseline generator for adversarial examples on the subset.
+- core/transfer_attack_core.py
+  - Main baseline attack implementations and shared utilities.
+- core/verification_thresholds.json
+  - Dataset-specific thresholds for the victim models.
+.
+
+Current strongest vanilla baselines on the provided subset
+- SI_NI_FGSM
+- MI_FGSM
+
+Evaluation rules
+- Skip self-transfer pairs.
+- If attacker is FaceNet512, also skip FaceNet as victim.
+- Use the thresholds in core/verification_thresholds.json.
+
+What a good contribution looks like
+- Clean implementation of a newer attack from recent literature.
+- Reproducible subset experiment.
+- Clear comparison against the current baselines.
+- Honest reporting of strengths, failures, and limitations.

att_results/att_all_victims_metrics.csv

@@ -0,0 +1,121 @@
+row_id,dataset,attack_type,victim_model,similarity,clean_similarity,threshold,breach,impact
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