13 citations · 15 across the 5 of their papers we have counts for
10 papers
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…
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…
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…
REALIS: A Curated Dataset for Studying the Challenges of AI Image Detection
Aleksandr Gushchin, Khaled Abud, Georgii Bychkov +5
AI-generated image detectors are often evaluated on benchmarks where real and synthetic images differ in content, quality, or generation artifacts, allowing models to rely on datas…
NTIRE 2026 Challenge on Robust AI-Generated Image Detection in the Wild
Aleksandr Gushchin, Khaled Abud, Ekaterina Shumitskaya +51
This paper presents an overview of the NTIRE 2026 Challenge on Robust AI-Generated Image Detection in the Wild, held in conjunction with the NTIRE workshop at CVPR 2026. The goal o…
IQA-Adapter: Exploring Knowledge Transfer from Image Quality Assessment to Diffusion-based Generative Models
Khaled Abud, Sergey Lavrushkin, Alexey Kirillov +1
Diffusion-based models have recently revolutionized image generation, achieving unprecedented levels of fidelity. However, consistent generation of high-quality images remains chal…
Exploring adversarial robustness of JPEG AI: methodology, comparison and new methods
Egor Kovalev, Georgii Bychkov, Khaled Abud +5
Adversarial robustness of neural networks is an increasingly important area of research, combining studies on computer vision models, large language models (LLMs), and others. With…
Guardians of Image Quality: Benchmarking Defenses Against Adversarial Attacks on Image Quality Metrics
Alexander Gushchin, Khaled Abud, Georgii Bychkov +7
In the field of Image Quality Assessment (IQA), the adversarial robustness of the metrics poses a critical concern. This paper presents a comprehensive benchmarking study of variou…
Comparing the Robustness of Modern No-Reference Image- and Video-Quality Metrics to Adversarial Attacks
Anastasia Antsiferova, Khaled Abud, Aleksandr Gushchin +3
Nowadays, neural-network-based image- and video-quality metrics perform better than traditional methods. However, they also became more vulnerable to adversarial attacks that incre…
Video compression dataset and benchmark of learning-based video-quality metrics
Anastasia Antsiferova, Sergey Lavrushkin, Maksim Smirnov +3
Video-quality measurement is a critical task in video processing. Nowadays, many implementations of new encoding standards - such as AV1, VVC, and LCEVC - use deep-learning-based d…