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Relationship between Model Compression and Adversarial Robustness: A Review of Current Evidence | IEEE Conference Publication | IEEE Xplore

Relationship between Model Compression and Adversarial Robustness: A Review of Current Evidence


Abstract:

Increasing the model capacity is a known approach to enhance the adversarial robustness of deep learning networks. On the other hand, various model compression techniques...Show More

Abstract:

Increasing the model capacity is a known approach to enhance the adversarial robustness of deep learning networks. On the other hand, various model compression techniques, including pruning and quantization, can reduce the size of the network while preserving its accuracy. Several recent studies have addressed the relationship between model compression and adversarial robustness, while some experiments have reported contradictory results. This work summarizes available evidence and discusses possible explanations for the observed effects.
Date of Conference: 05-08 December 2023
Date Added to IEEE Xplore: 01 January 2024
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Conference Location: Mexico City, Mexico
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I. Introduction and Related Work

Goodfellow et al. [1] and Szegedy et al. [2] first brought up the risk of adversarial attacks, small perturbations (often imperceptible by humans) that are carefully crafted and added to the input of state-of-the-art (SOTA) deep neural networks (DNNs). Without specific DNN training or mitigation mea-sures, these attacks lead to high-confidence wrong outputs of SOTA DNNs and convolutional neural networks (CNNs). This inherent vulnerability of DNN s poses an especially high risk when applying them in autonomous driving, facial recognition, or medical domains.

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Svetlana Pavlitska, Oliver Bagge, Federico Peccia, Toghrul Mammadov, J. Marius Zöllner, "Iterative Filter Pruning for Concatenation-based CNN Architectures", 2024 International Joint Conference on Neural Networks (IJCNN), pp.1-10, 2024.

References

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