A Simple Transformer Pipeline for Full-Key Side-Channel Attacks on Uncropped Datasets
Deep learning-based side-channel analysis has historically focused on single-byte targets and manually cropped traces, which risks discarding exploitable leakage. While recent work has proposed specialized architectures and resampling techniques to address this gap, the literature lacks a simple transformer baseline for simultaneous full-key attacks on uncropped traces. We present an open-source transformer implementation for uncropped full-key attacks which uses the standard transformer encoder backbone, adapting only the input and output layers to the side-channel setting. We release our implementation, training recipes, and pretrained weights for uncropped ASCADv1f, ASCADv1r, and CHES-...
| Authors | Jimmy Gammell, Kaushik Roy |
|---|---|
| Primary category | cs.CR |
| Published | 31 August 2026 |
| Last updated at source | 31 August 2026 |
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