arXiv:2608.21837v1 Announce Type: new Abstract: Bitstream-corrupted Harsh Visual Understanding (BcHVU) aims to understand harshly degraded videos originally decoded from a severely corrupted bitstrea...
arXiv:2610.08414v1 Announce Type: new Abstract: Image Bitstream Fine-grained Understanding (IBFU) aims to directly perform fine-grained classification and semantic description generation from encoded...
arXiv:2608.29212v1 Announce Type: cross Abstract: Existing video watermarking systems are symmetric: the party that can verify a mark holds the extractor weights or generator secret and can therefore...
arXiv:2609.39623v1 Announce Type: new Abstract: The proliferation of high-fidelity generative editing models has made it possible to inject violent or sexual content into otherwise ordinary images wh...
COVER is a new video watermarking method that targets codec compression as its primary design goal. It embeds the watermark payload in the latent space of a frozen generative video autoencoder and recovers it by re‑encoding the received video into the same latent space. Using a differentiable codec surrogate bank, COVER achieves high bit accuracy across multiple codecs while keeping marked videos visually close to the originals.
arXiv:2410. 19553v2 Announce Type: replace-cross Abstract: This paper explores the impact of occlusions in video action detection.
ManiVid introduces a unified forensic analysis framework for manipulated videos, combining forgery detection, artifact grounding, and anomaly explanation. The authors release ManiVid-38K, a large dataset of 19K real‑fake video pairs with authenticity labels, forgery masks, and explanations, and a benchmark ManiVidBench with 1K balanced pairs. ManiVidLens, the proposed model, outperforms existing methods in artifact grounding and anomaly explanation while matching state‑of‑the‑art detection accuracy.
arXiv:2606. 08063v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) have demonstrated remarkable success in visual understanding, yet their performance degrades significantly under real-world visual corruptions.
arXiv:2605.12006v2 Announce Type: replace Abstract: The performance of promptable video object segmentation (PVOS) models substantially degrades under input corruptions, which prevents PVOS deploymen...
arXiv:2606. 02120v1 Announce Type: cross Abstract: In this report, we address the problem of determining whether a user performs an action incorrectly from egocentric video data.
The paper proposes a unified forensics framework that extends traditional binary image manipulation detection to a multiclass setting—distinguishing real, fully synthetic, and tampered images. It adds a segmentation branch for pixel‑level localization of tampered regions, achieving higher classification accuracy and IoU scores compared to recent benchmarks. The authors provide the implementation on GitHub for reproducibility.
arXiv:2609.31558v1 Announce Type: new Abstract: Contrastive Language--Image Pretraining (CLIP) has emerged as a dominant vision backbone due to its strong transferability and zero-shot capabilities....