arXiv Computer Vision
Scalable Black-Box Model Attribution for Images
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
Related stories
Hugging Face Trending Papers
Aug 16
Scalable Black-Box Model Attribution for Images
The rapid proliferation of generative models raises the model attribution problem: given only an image, can we determine which model produced it? Existing methods have grown as elaborate as the genera...
arXiv AI
Jun 12
Proto-LeakNet: Towards Signal-Leak Aware Attribution in Synthetic Human Face Imagery
arXiv:2511. 04260v3 Announce Type: replace-cross Abstract: The growing sophistication of synthetic image and deepfake generation models has turned source attribution and authenticity verification into a critical challenge for modern computer vision systems.
arXiv Computer Vision
Sep 15
Learning Continuous Source Responses For Generalizable AI-Generated Image Detection
arXiv:2609.14316v1 Announce Type: new Abstract: Advances in image generation have made synthetic images increasingly difficult to distinguish from real photographs, raising concerns about the trustwo...
arXiv Machine Learning
Jun 30
$\mu$Flow: Leveraging Average Images for Improving Generalisation of Deepfake Faces Detectors
arXiv:2606. 30528v1 Announce Type: cross Abstract: Current generative models, including GANs and diffusion models, have reached an outstanding level of photorealism, posing significant risks to privacy and security.
arXiv Computer Vision
Sep 4
Beyond Small Patches: Black-Box Detection and Purification of Diverse Backdoor Triggers
The paper introduces TRIM, a black‑box defense for backdoor attacks in computer vision models. TRIM identifies and removes malicious trigger regions at inference time using region‑based segmentation, adaptive trigger discovery via inpainting and diffusion, and selective purification, without needing model internals, training data, or clean samples. Experiments on various datasets and trigger types show TRIM reduces attack success rates to as low as 1.16% while maintaining high clean accuracy.
arXiv Machine Learning
Jul 15
RAFP: Identifying LLM Lineages via Rare-Region Fingerprints
arXiv:2505. 12682v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly released under restricted licenses, creating a growing need for robust model ownership verification.