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cs.CV2026

Multi-Task Consistency-based Detection of Adversarial Attacks

Cong Chen, Jean-Philippe Monteuuis, Jonathan Petit

Deep Neural Networks (DNNs) have found successful deployment in numerous vision perception systems. However, their susceptibility to adversarial attacks has prompted concerns regar…

cs.CV2026

Systematic Discovery of Semantic Attacks in Online Map Construction through Conditional Diffusion

Chenyi Wang, Ruoyu Song, Raymond Muller +5

Autonomous vehicles depend on online HD map construction to perceive lane boundaries, dividers, and pedestrian crossings -- safety-critical road elements that directly govern motio…

cs.CV2026

On the Robustness of Watermarking for Autoregressive Image Generation

Andreas Müller, Denis Lukovnikov, Shingo Kodama +5

The proliferation of autoregressive (AR) image generators demands reliable detection and attribution of their outputs to mitigate misinformation, and to filter synthetic images fro…

cs.CV2025

Physical ID-Transfer Attacks against Multi-Object Tracking via Adversarial Trajectory

Chenyi Wang, Yanmao Man, Raymond Muller +4

Multi-Object Tracking (MOT) is a critical task in computer vision, with applications ranging from surveillance systems to autonomous driving. However, threats to MOT algorithms hav…

cs.CV2025

Uncertainty Quantification for Collaborative Object Detection Under Adversarial Attacks

Huiqun Huang, Cong Chen, Jean-Philippe Monteuuis +2

Collaborative Object Detection (COD) and collaborative perception can integrate data or features from various entities, and improve object detection accuracy compared with individu…