Research map / Adversarial robustness and security / Black-box and transfer attacks
Black-box Adversarial Attacks with Limited Queries and Information
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use NES gradient estimates with PGD updates in the query-limited setting | InceptionV3 · ImageNet test set | success rate: 99.2% | Helped |
| Use NES gradient estimates with PGD updates in the query-limited setting | InceptionV3 · ImageNet test set | median queries: 11550 | Helped |
| Initialize from a target-class image and alternate blending toward the source with target-class likelihood maximization | InceptionV3 · ImageNet test set | success rate: 93.6% | Helped |
| Initialize from a target-class image and alternate blending toward the source with target-class likelihood maximization | InceptionV3 · ImageNet test set | median queries: 49624 | Helped |
| Use robustness to random perturbations as a proxy score when only ranked labels are available | InceptionV3 · ImageNet test set | success rate: 90% | Helped |
| Use robustness to random perturbations as a proxy score when only ranked labels are available | InceptionV3 · ImageNet test set | median queries: 2.7e6 | Helped |
Closest papers by meaning
- Understanding the Robustness of Randomized Feature Defense Against Query-Based Adversarial Attacks (ICLR 2024 · similarity 0.91)
- Blackbox Attacks via Surrogate Ensemble Search (NeurIPS 2022 · similarity 0.90)
- Beyond ImageNet Attack: Towards Crafting Adversarial Examples for Black-box Domains (ICLR 2022 · similarity 0.90)
- Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models (ICLR 2018 · similarity 0.90)
- BRUSLEATTACK: A QUERY-EFFICIENT SCORE- BASED BLACK-BOX SPARSE ADVERSARIAL ATTACK (ICLR 2024 · similarity 0.88)
- Mind the Gap: Detecting Black-box Adversarial Attacks in the Making through Query Update Analysis (CVPR 2025 · similarity 0.87)
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