Institution profile

CRISTAL

Academic institutioneurope · fr
Official website
Research library5linked papers
Opportunities0open roles
Selected work

Representative Papers

Maximize margins for robust splicing detection

Jul 28, 2025

Current image splicing detection models exhibit poor generalization against post-processing operations (e.g., JPEG compression, Gaussian filtering), severely undermining their reliability in real-world deployment. To address this, we propose a robust training paradigm grounded in latent-space decision boundary analysis: model robustness is quantified via boundary width, and models are jointly trained under multiple post-processing perturbations; the optimal checkpoint is selected on the validation set based on maximal boundary width. Crucially, this approach requires no architectural modifications or loss-function redesign—robustness is enhanced solely through refined training strategies and boundary-aware model selection, thereby improving discriminability in the feature space. Extensive experiments across multiple benchmark datasets demonstrate that our method significantly enhances resilience to common post-processing artifacts. Specifically, it yields average AUC improvements of 3.2–5.8 percentage points over state-of-the-art training strategies under JPEG compression and Gaussian blur.

0 citationsRead paper
Recent publications

Latest Papers

Maximize margins for robust splicing detection

Jul 28, 2025

Current image splicing detection models exhibit poor generalization against post-processing operations (e.g., JPEG compression, Gaussian filtering), severely undermining their reliability in real-world deployment. To address this, we propose a robust training paradigm grounded in latent-space decision boundary analysis: model robustness is quantified via boundary width, and models are jointly trained under multiple post-processing perturbations; the optimal checkpoint is selected on the validation set based on maximal boundary width. Crucially, this approach requires no architectural modifications or loss-function redesign—robustness is enhanced solely through refined training strategies and boundary-aware model selection, thereby improving discriminability in the feature space. Extensive experiments across multiple benchmark datasets demonstrate that our method significantly enhances resilience to common post-processing artifacts. Specifically, it yields average AUC improvements of 3.2–5.8 percentage points over state-of-the-art training strategies under JPEG compression and Gaussian blur.

0 citationsRead paper