Synthetic & Adversarial ForEnsics
(SAFE)

Topics: Synthetic media detection, adversarial attacks, multimedia forensics, robustness, AI security, generative models

We invite original research contributions in (but not limited to) the following areas:

• Generative image and video models (e.g., diffusion models, VLMs, GANs) and foundation models for synthetic media creation, manipulation, and robustness evaluation
• Defensive fingerprinting and watermarking of generative models for source attribution
• Red teaming methods for generative models, including realistic adversarial simulation environments
• Provenance tracking and authenticity verification of digital media
• Detection of synthetic media (e.g., AI-generated images, and video)
• Adversarial attack, detection, and mitigation methods for visual data including video streams
• Training data poisoning detection and mitigation for generative models
• Joint modeling of synthetic media and adversarial threats
• Benchmarks, datasets, and evaluation metrics for adversarial detection and robustness
• Generative and foundation model explanation and interpretation
• Adversarial attack and mitigation techniques for distilled models
• Ethical, societal, and legal implications of synthetic and adversarial media

We will accept full papers (6 to 8 pages, excluding references) as oral presentations and will be included in the CVPR 2026 Workshop Proceedings using the appropriate CVPR 2026 formatting guidelines. Short papers and extended abstracts (2 to 4 pages, excluding references) will be accepted as oral presentations but will not be archival.

All submissions will undergo a double-blind peer review to ensure fairness and impartiality in the evaluation process.