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20192026
most citedVideo compression dataset and benchmark of learning-based video-quality metrics

13 citations · 47 across the 35 of their papers we have counts for

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13 papers · 1 filter

cs.CV2026

MSU Team at the Explainable Deepfake Detection Challenge 2026: Grounded Artifact Evidence for Deepfake Detection

Artem Filippov, Aleksandr Gushchin, Kirill Koltsov +2

Recent advances in generative image models have made many manipulated images highly realistic, raising the need for detectors that are not only accurate but also able to provide vi…

cs.CV2026

DISRQAD: Diffusion Image Super-Resolution Quality Assessment Dataset and Benchmark

Nikita Kukuzei, Artem Borisov, Evgeney Bogatyrev +3

Diffusion-based image super-resolution (SR) can create visually plausible detail that is not supported by the low-resolution input. We introduce DISRQAD, a subjective-quality datas…

cs.CV2026

VCR-Bench: A Modular Open-Source Benchmark for Video Classification Robustness

Maksim Plinskiy, Aleksandr Gushchin, Sergey Lavrushkin +2

Robustness of image classification has several benchmarks, but their video counterparts are absent. In video classification temporal dimension introduces additional degrees of free…

cs.CV2026

TSRN-RTVD: Real-Time Video Deblurring System

Nikita Alutis, Danila Evsyukov, Egor Chistov +3

As video capture moves to handheld and edge devices, motion blur from camera shake has become a pervasive degradation that lowers perceptual quality and harms downstream vision tas…

cs.CV2026

Exploring Weaknesses of Generative Image Watermarks against Latent Frequency Masking

Kirill Aistov, Khaled Abud, Irina Serzhenko +8

Invisible watermarking has become a central tool for tracing AI-generated images, but its robustness against adaptive removal attacks remains an open security question. We introduc…

cs.CV2026

WARP: A Unified Benchmark for Invisible Image Watermarking -- Robustness and Protection Against Attacks

Khaled Abud, Aleksey Yakushev, Aleksandr Akimenkov +9

Digital image watermarking is increasingly critical in media contexts, as emerging regulations and industry practices require marking AI-generated content and ensuring traceable so…