Cheng Gong- City University of Hong Kong
Cheng Gong
- City University of Hong Kong
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Crafting adversarial examples is crucial for evaluating and enhancing the robustness of Deep Neural Networks (DNNs), presenting a challenge equivalent to maximizing a non-differentiable 0-1 loss function. However, existing single objective methods, namely adversarial attacks focus on a surrogate loss function, do not fully harness the benefits of e...
The escalating threat of adversarial attacks on deep learning models, particularly in security-critical fields, has highlighted the need for robust deep learning systems. Conventional evaluation methods of their robustness rely on adversarial accuracy, which measures the model performance under a specific perturbation intensity. However, this singu...
The escalating threat of adversarial attacks on deep learning models, particularly in security-critical fields, has underscored the need for robust deep learning systems. Conventional robustness evaluations have relied on adversarial accuracy, which measures a model's performance under a specific perturbation intensity. However, this singular metri...
