HENet: A Heterogeneous Encoding Network for General and Robust Adversarial Example Generation

Summary

HENet, a dual-branch generative model, is proposed to build a robust generator-based adversarial attack framework against deep neural networks. Its Adaptive Feature Fusion Module addresses dimension and representativeness mismatches between CNNs and Transformers, while a Dynamic Differentiable JPEG Simulator uses an adaptive quantization mask to improve robustness under JPEG compression. The authors report a better attack success rate, lower perturbation magnitude and higher robustness across target architectures.

Topics

• Adversarial machine learning

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