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Found 5 papers, 2 papers with code
Explanation as a Watermark: Towards Harmless and Multi-bit Model Ownership Verification via Watermarking Feature Attribution
Motivated by this understanding, we design a new watermarking paradigm, $i. e.$, Explanation as a Watermark (EaaW), that implants verification behaviors into the explanation of feature attribution instead of model predictions.
Going Proactive and Explanatory Against Malware Concept Drift
To ensure a comprehensive response to concept drift, it facilitates a coordinated update process for both the classifier and the detector.
RetouchUAA: Unconstrained Adversarial Attack via Image Retouching
The former custom-designed human-interpretability retouching framework for adversarial attack by linearizing images while modelling the local processing and retouching decision-making in human retouching behaviour, provides an explicit and reasonable pipeline for understanding the robustness of DNNs against retouching.
Pitfalls in Language Models for Code Intelligence: A Taxonomy and Survey
After carefully examining these studies, we designed a taxonomy of pitfalls in LM4Code research and conducted a systematic study to summarize the issues, implications, current solutions, and challenges of different pitfalls for LM4Code systems.
FINER: Enhancing State-of-the-art Classifiers with Feature Attribution to Facilitate Security Analysis
Although feature attribution (FA) methods can be used to explain deep learning, the underlying classifier is still blind to what behavior is suspicious, and the generated explanation cannot adapt to downstream tasks, incurring poor explanation fidelity and intelligibility.
