Research map / Adversarial robustness and security / Black-box and transfer attacks
Boosting Black-Box Attack With Partially Transferred Conditional Adversarial Distribution
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use transferred c-Glow mapping parameters and adapt Gaussian parameters with target queries | CIFAR-10 untargeted attacks · ResNet, DenseNet, VGG, and PyramidNet targets | mean queries: 135.1 -> 81.6; 111.7 -> 43.3; 125.5 -> 56.4; 82.3 -> 30.1 | Helped |
| Use transferred c-Glow mapping parameters and adapt Gaussian parameters with target queries | CIFAR-10 targeted attacks to class 0 · ResNet, DenseNet, VGG, and PyramidNet targets | mean queries: 894.1 -> 696.4; 826.9 -> 787.1; 1343.2 -> 861.1; 865.8 -> 651.2 | Helped |
| Use transferred c-Glow mapping parameters and adapt Gaussian parameters with target queries | CIFAR-10 untargeted attacks · ResNet, DenseNet, VGG, and PyramidNet targets | ASR: 100.0; 100.0; 99.9; 100.0 | Helped |
| Use transferred c-Glow mapping parameters and adapt Gaussian parameters with target queries | CIFAR-10 targeted attacks to class 0 · ResNet, DenseNet, VGG, and PyramidNet targets | ASR: 99.9; 100.0; 98.8; 98.9 | Mixed |
Closest papers by meaning
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- LRS: Enhancing Adversarial Transferability through Lipschitz Regularized Surrogate (AAAI 2024 · similarity 0.90)
- Understanding the Robustness of Randomized Feature Defense Against Query-Based Adversarial Attacks (ICLR 2024 · similarity 0.89)
- Enhancing Adversarial Transferability with Adversarial Weight Tuning (AAAI 2025 · similarity 0.89)
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