Author

Mark Stamp

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Preprint Aug 2026

Concept Drift Detection and Adaptive Retraining of Malware Classification Models

This chapter analyzes two machine learning-based approaches to automated concept drift detection-a novel approach based on One-Class Support Vector Machines (OCSVM) and a previously-studied technique based on Minibatch K-Means (MK-Means), which achieve classification accuracy comparable to periodic retraining, while of...

C. Chungata, Martin Jurecek, Katerina Potika et al. · 0 citations

Preprint Aug 2026

A Comparison of Malware Image Transformations Using Grad-CAM and Hybrid Learning Models

A key finding of this research is that for the malware image transformations considered, accuracy and explanation faithfulness do not coincide, e.g., image transformation techniques that produce the most faithful explanations yield only mid-tier accuracy.

Vibha Bhavikatti, Mark Stamp · 0 citations

Preprint Open access Aug 2026

Robustness of AI-Art Detectors under Generator Shift

Text-to-image generative models have advanced rapidly, with modern Diffusion Transformer architectures producing images that are increasingly difficult to distinguish from human-created artwork. This development has raised significant concerns regarding copyright protection, misinformation, fraud, impersonation, and th...

S. S. Thakur, Meien Li, Mark Stamp · 0 citations