7 citations · 9 across the 8 of their papers we have counts for
12 papers
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…
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…
Team MSU GenText-Forensics Challenge 2026 Technical Report
Kirill Koltsov, Aleksandr Gushchin, Dmitriy Vatolin +1
Document text forgery has evolved beyond simple pixel-level manipulation: modern attacks alter not only the appearance of a document but also its meaning, and increasingly target t…
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…
TIQA: Human-Aligned Perceptual Text Quality Assessment in Generated Images
Kirill Koltsov, Aleksandr Gushchin, Anastasia Antsiferova +1
Recent text-to-image models have improved global realism, but text rendering remains a persistent failure mode: images may look convincing overall, yet local typography often conta…
BiRQA: Bidirectional Robust Quality Assessment for Images
Aleksandr Gushchin, Dmitriy S. Vatolin, Anastasia Antsiferova
Full-Reference image quality assessment (FR IQA) is important for image compression, restoration and generative modeling, yet current neural metrics remain slow and vulnerable to a…
LEHA-CVQAD: Dataset To Enable Generalized Video Quality Assessment of Compression Artifacts
Aleksandr Gushchin, Maksim Smirnov, Dmitriy Vatolin +1
We propose the LEHA-CVQAD (Large-scale Enriched Human-Annotated Compressed Video Quality Assessment) dataset, which comprises 6,240 clips for compression-oriented video quality ass…
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…
AIM 2024 Challenge on Compressed Video Quality Assessment: Methods and Results
Maksim Smirnov, Aleksandr Gushchin, Anastasia Antsiferova +29
Video quality assessment (VQA) is a crucial task in the development of video compression standards, as it directly impacts the viewer experience. This paper presents the results of…
Adversarial purification for no-reference image-quality metrics: applicability study and new methods
Aleksandr Gushchin, Anna Chistyakova, Vladislav Minashkin +2
Recently, the area of adversarial attacks on image quality metrics has begun to be explored, whereas the area of defences remains under-researched. In this study, we aim to cover t…
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…