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cs.CV2025
RAID: A Dataset for Testing the Adversarial Robustness of AI-Generated Image Detectors
Hicham Eddoubi, Jonas Ricker, Federico Cocchi +7
AI-generated images have reached a quality level at which humans are incapable of reliably distinguishing them from real images. To counteract the inherent risk of fraud and disinf…
cs.CV2024
Robust image classification with multi-modal large language models
Francesco Villani, Igor Maljkovic, Dario Lazzaro +3
Deep Neural Networks are vulnerable to adversarial examples, i.e., carefully crafted input samples that can cause models to make incorrect predictions with high confidence. To miti…
cs.CV2019
Deep Neural Rejection against Adversarial Examples
Angelo Sotgiu, Ambra Demontis, Marco Melis +4
Despite the impressive performances reported by deep neural networks in different application domains, they remain largely vulnerable to adversarial examples, i.e., input samples t…