Florian Yger, Florent Tariolle
We lifted 23 functions out of this paper's own repositories and ran 20 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| Tariolle/opportunistic-target-selection | canonical | 20 of 23 |
| Function | Status | Where it lives |
|---|---|---|
| analyze_overlap | Ran | Tariolle/opportunistic-target-selection/analysis/analyze_target_overlap.py code served (permissive licence) · get_code("195dacdd23c77447") |
| bootstrap_cdf | Ran | Tariolle/opportunistic-target-selection/analysis/analyze_winrate.py code served (permissive licence) · get_code("1b6b002e7fb34228") |
| build_configs | Ran | Tariolle/opportunistic-target-selection/analysis/analyze_margin.py code served (permissive licence) · get_code("17d6723584b9dc2f") |
| build_pairs | Ran | Tariolle/opportunistic-target-selection/analysis/analyze_lockmatch.py code served (permissive licence) · get_code("ec51ead6890067f1") |
| compute_entropy_trajectory | Ran | Tariolle/opportunistic-target-selection/analysis/analyze_robust_landscape.py code served (permissive licence) · get_code("e56096db231fc40b") |
| compute_perseed_tests | Ran | Tariolle/opportunistic-target-selection/analysis/analyze_multiseed.py code served (permissive licence) · get_code("ecc4531cfdb06095") |
| compute_pooled_test | Ran | Tariolle/opportunistic-target-selection/analysis/analyze_multiseed.py code served (permissive licence) · get_code("9d7f8c83b6f60a92") |
| fig_lockmatch_savings | Ran | Tariolle/opportunistic-target-selection/analysis/analyze_lockmatch.py code served (permissive licence) · get_code("bee3bb1c9640269f") |
| fig_overlap | Ran | Tariolle/opportunistic-target-selection/analysis/analyze_target_overlap.py code served (permissive licence) · get_code("1014490494228531") |
| fig_per_model | Ran | Tariolle/opportunistic-target-selection/analysis/analyze_benchmark.py code served (permissive licence) · get_code("0fd074a6d3f951eb") |
| filter_runs | Ran | Tariolle/opportunistic-target-selection/analysis/analyze_robust_landscape.py code served (permissive licence) · get_code("fee328f1f86bd7a6") |
| load_csv | Ran | Tariolle/opportunistic-target-selection/analysis/analyze_lockmatch.py code served (permissive licence) · get_code("7e89e69f7971931c") |
| load_data | Ran | Tariolle/opportunistic-target-selection/analysis/analyze_benchmark.py code served (permissive licence) · get_code("43e33823b698bde0") |
| load_data | Ran | Tariolle/opportunistic-target-selection/analysis/analyze_oracle_beat.py code served (permissive licence) · get_code("6c723a46b19147df") |
| load_data | Ran | Tariolle/opportunistic-target-selection/analysis/analyze_winrate.py code served (permissive licence) · get_code("b7714b19c17b22dd") |
| load_existing_keys | Ran | Tariolle/opportunistic-target-selection/benchmarks/ablation_target_selection.py code served (permissive licence) · get_code("8cb86b7f243f1725") |
| load_json_runs | Ran | Tariolle/opportunistic-target-selection/analysis/analyze_robust_landscape.py code served (permissive licence) · get_code("480b7c94873ce0ad") |
| load_margin_csv | Ran | Tariolle/opportunistic-target-selection/analysis/analyze_margin.py code served (permissive licence) · get_code("22c29bc74df94117") |
| load_winrate_csv | Ran | Tariolle/opportunistic-target-selection/analysis/analyze_margin.py code served (permissive licence) · get_code("c51b017253ac26a9") |
| summarize | Ran | Tariolle/opportunistic-target-selection/analysis/analyze_oracle_beat.py code served (permissive licence) · get_code("f765883ce1ccc896") |
| compute_oracle_beat | Not yet run | Tariolle/opportunistic-target-selection/analysis/analyze_oracle_beat.py code served (permissive licence) · get_code("0aec3be9ec99d4a6") |
| fig_headline_bars | Not yet run | Tariolle/opportunistic-target-selection/analysis/analyze_benchmark.py code served (permissive licence) · get_code("10113eb6963af894") |
| select_clean_argmax | Not yet run | Tariolle/opportunistic-target-selection/benchmarks/ablation_target_selection.py code served (permissive licence) · get_code("d904b3c28e4a6340") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Black-box adversarial attacks that minimize only the ground-truth confidence suffer from class drift: perturbations wander through the feature space without committing to a specific adversarial class, wasting queries on diffuse, undirected progress. We introduce Opportunistic Target Selection (OTS), a lightweight wrapper that switches an untargeted attack to a targeted objective early in its trajectory, locking onto whichever non-true class currently leads. OTS requires no architectural modification to the underlying attack, no gradient access, and no a priori target-class knowledge. We validate OTS on three score-based attacks (SimBA, Square Attack with cross-entropy loss, and Bandits) across five standard ImageNet classifiers (4,500 runs). On random-search attacks, OTS closely tracks oracle performance, with gains up to +27 pp in success rate and 43% relative reduction in censored-mean iterations on ResNet-50. On gradient-estimation attacks (Bandits) and attacks with margin loss, OTS is redundant, a negative result that reinforces our interpretation of OTS as a margin-loss surrogate. On adversarially-trained models, a bimodal difficulty distribution eliminates the regime where targeting helps. We release our code at https://github.com/Tariolle/opportunistic-target-selection.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2605.25663")
get_code_for_paper("2605.25663")
have("2605.25663")
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