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Bit-stream recognition (BSR) has many applications, such as forensicinvestigations, detection of copyright infringement, and malware analysis. Wepropose the first BSR that takes a bare input bit-stream and outputs a classlabel without any preprocessing. To achieve our goal, we propose a centrifugemechanism, where the upstream layers (sub-net) capture global features and tellthe downstream layers (main-net) to switch the focus, even if a part of theinput bit-stream has the same value. We applied the centrifuge mechanism tocompiler provenance recovery, a type of BSR, and achieved excellentclassification. Additionally, downstream transfer learning (DTL), one of thelearning methods we propose for the centrifuge mechanism, pre-trains themain-net using the sub-net's ground truth instead of the sub-net's output. Wefound that sub-predictions made by DTL tend to be highly accurate when thesub-label classification contributes to the essence of the main prediction.

Compiler Provenance Recovery for Multi-CPU Architectures Using a Centrifuge Mechanism