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Analysis Artefacts -- Double-Strike: Breaking Approximation-based Side-Channel Countermeasures for DNNs

Authors: Casalino, Lorenzo;

Analysis Artefacts -- Double-Strike: Breaking Approximation-based Side-Channel Countermeasures for DNNs

Abstract

# Analysis Artefacts for the Research Paper: "Double Strike: Breaking Approximation-based Side-Channel Countermeasures for DNNs" This archive contains, for each studied implementation, the: - Side-channel traces; - Inputs; - Random values fed to the MACPruning mechanism to skip the non-important; weights (only for the protected implementation); - Sequence of executed MAC/important weights (only for the circumvented/protected implementation); - Parameters characterising each side-channel acquisition. This archive complements the published code artefacts ("https://github.com/DoktorC/double-strike-host2026") for the paper"Double Strike: Breaking Approximation-based Side-Channel Countermeasures for DNNs" (To appear in HOST 2026).

Keywords

Deep Neural Networks, Side-Channel Analysis, Hardware Security, Approximate Computing, Embedded Systems, Artefacts

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average