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Found 4 papers, 2 papers with code
Boosting Cross-task Transferability of Adversarial Patches with Visual Relations
The transferability of adversarial examples is a crucial aspect of evaluating the robustness of deep learning systems, particularly in black-box scenarios.
Benchmarking the Robustness of Quantized Models
To address this gap, we thoroughly evaluated the robustness of quantized models against various noises (adversarial attacks, natural corruptions, and systematic noises) on ImageNet.
Harnessing Perceptual Adversarial Patches for Crowd Counting
Crowd counting, which has been widely adopted for estimating the number of people in safety-critical scenes, is shown to be vulnerable to adversarial examples in the physical world (e. g., adversarial patches).
Dual Attention Suppression Attack: Generate Adversarial Camouflage in Physical World
Deep learning models are vulnerable to adversarial examples.
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