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Found 4 papers, 2 papers with code
Dataset Inference for Self-Supervised Models
We introduce a new dataset inference defense, which uses the private training set of the victim encoder model to attribute its ownership in the event of stealing.
On the Difficulty of Defending Self-Supervised Learning against Model Extraction
We construct several novel attacks and find that approaches that train directly on a victim's stolen representations are query efficient and enable high accuracy for downstream models.
A Zest of LIME: Towards Architecture-Independent Model Distances
In this paper, we instead propose to compute distance between black-box models by comparing their Local Interpretable Model-Agnostic Explanations (LIME).
XDA: Accurate, Robust Disassembly with Transfer Learning
We present XDA, a transfer-learning-based disassembly framework that learns different contextual dependencies present in machine code and transfers this knowledge for accurate and robust disassembly.
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