CAPM: Fast and Robust Verification on Maxpool-based CNN via Dual Network
<|reference_start|>CAPM: Fast and Robust Verification on Maxpool-based CNN via Dual Network: This study uses CAPM (Convex Adversarial Polytope for Maxpool-based CNN) to improve the verified bound for general purpose maxpool-based convolutional neural networks (CNNs) under bounded norm adversarial perturbations. The maxpool function is decomposed as a series of ReLU functions to extend the convex relaxation technique to maxpool functions, by which the verified bound can be efficiently computed through a dual network. The experimental results demonstrate that this technique allows the state-of-the-art verification precision for maxpool-based CNNs and involves a much lower computational cost than current verification methods, such as DeepZ, DeepPoly and PRIMA. This method is also applicable to large-scale CNNs, which previous studies show to be often computationally prohibitively expensive. Under certain circumstances, CAPM is 40-times, 20-times or twice as fast and give a significantly higher verification bound (CAPM 98% vs. PRIMA 76%/DeepPoly 73%/DeepZ 8%) as compared to PRIMA/DeepPoly/DeepZ. Furthermore, we additionally present the time complexity of our algorithm as $O(W^2NK)$, where $W$ is the maximum width of the neural network, $N$ is the number of neurons, and $K$ is the size of the maxpool layer's kernel.<|reference_end|>
@article{bai2024capm:,
    title={CAPM: Fast and Robust Verification on Maxpool-based CNN via Dual Network},
    author={Jia-Hau Bai, Chi-Ting Liu, Yu Wang, Fu-Chieh Chang, Pei-Yuan Wu},
    journal={arXiv preprint arXiv:2407.09550},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.09550},
    primaryClass={cs.CV cs.AI cs.LG}
}