# Power side-channel leakage localization through adversarial training of deep neural networks

arXiv: https://arxiv.org/abs/2410.22425 · HTML: https://mlsanity.com/paper/2410.22425/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 59 references that are in the map.
- First posted on arXiv 29 Oct 2024.

## Builds on (most cited first, 21 of 21)

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- [Object Region Mining with Adversarial Erasing: A Simple Classification to Semantic Segmentation Approach](https://mlsanity.com/paper/1703.08448/) (2017)
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- [Controllable Invariance through Adversarial Feature Learning](https://mlsanity.com/paper/1705.11122/) (2017)
- [What shapes feature representations? Exploring datasets, architectures, and training](https://mlsanity.com/paper/2006.12433/) (2020)
- [Representation via Representations: Domain Generalization via Adversarially Learned Invariant Representations](https://mlsanity.com/paper/2006.11478/) (2020)
- [FairFaceGAN: Fairness-aware Facial Image-to-Image Translation](https://mlsanity.com/paper/2012.00282/) (2020)
- [Enhancing the Performance of Practical Profiling Side-Channel Attacks Using Conditional Generative Adversarial Networks](https://mlsanity.com/paper/2007.05285/) (2020)

## Abstract (arXiv)

Supervised deep learning has emerged as an effective tool for carrying out power side-channel attacks on cryptographic implementations. While increasingly-powerful deep learning-based attacks are regularly published, comparatively-little work has gone into using deep learning to defend against these attacks. In this work we propose a technique for identifying which timesteps in a power trace are responsible for leaking a cryptographic key, through an adversarial game between a deep learning-based side-channel attacker which seeks to classify a sensitive variable from the power traces recorded during encryption, and a trainable noise generator which seeks to thwart this attack by introducing a minimal amount of noise into the power traces. We demonstrate on synthetic datasets that our method can outperform existing techniques in the presence of common countermeasures such as Boolean masking and trace desynchronization. Results on real datasets are weak because the technique is highly sensitive to hyperparameters and early-stop point, and we lack a holdout dataset with ground truth knowledge of leaking points for model selection. Nonetheless, we believe our work represents an important first step towards deep side-channel leakage localization without relying on strong assumptions about the implementation or the nature of its leakage. An open-source PyTorch implementation of our experiments is provided.