AI 中文总结
针对DL-SCA跨设备性能下降问题，提出s-MDM生成框架，通过cVAE和风格调制合成虚拟设备，在偏移目标上实现稳定低关键排名。
AI 中文摘要
基于深度学习的侧信道分析（DL-SCA）经常因印刷电路板布线差异、硅工艺变化和测量噪声偏移而在未见过的硬件上遭受灾难性的性能下降。本海报提出了合成多设备模型（s-MDM），一种零目标迹线生成框架，旨在提高跨设备便携性。s-MDM结合了结构化cVAE生成器、Walsh-Hadamard泄漏锚点、连续风格调制以及解耦的泄漏风格-域判别器，离线合成虚拟源设备配置文件。在32位侧信道迹线（AES_PTv2）上进行基准测试，s-MDM描绘了一个精确的操作边界：虽然物理MDM在相同电气克隆（D4）上仍然优越，但s-MDM在布局/采集偏移的Pinata目标上实现了持续较低的关键排名，而物理基线在该目标上不稳定或错位。
英文摘要
Deep Learning-based Side-Channel Analysis (DL-SCA) frequently suffers from catastrophic performance degradation across unseen hardware due to printed circuit board routing differences, silicon process variations, and measurement noise shifts. This poster presents the Synthetic Multiple Device Model (s-MDM), a zero-target-trace generative framework designed to improve cross-device portability. s-MDM combines a structured cVAE generator, a Walsh-Hadamard leakage anchor, continuous style modulation, and decoupled leakage-style--domain critics to synthesize virtual source-device profiles offline. Benchmarked on 32-bit side-channel traces (AES_PTv2), s-MDM maps a precise operational boundary: while physical MDM remains superior on identical electrical clones (D4), s-MDM achieves consistently low key rank on the layout/acquisition-shifted Pinata target, where physical baselines are unstable or misaligned.
