13 citations · 47 across the 27 of their papers we have counts for
33 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…
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…
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…
Wall-Clock Complexity for Zeroth-Order Optimization with Tunable Oracle Fidelity
Alexandra Suvorikova, Igor Pavlov, Artem Vasin +4
Zeroth-order (black-box) optimization is applied when gradients are unavailable and objective evaluations rely on expensive simulations. In many such applications, the oracle fidel…
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…
From Pixels to Reality: Physical-Digital Patch Attacks on Real-World Camera
Victoria Leonenkova, Ekaterina Shumitskaya, Dmitriy Vatolin +1
This demonstration presents Digital-Physical Adversarial Attacks (DiPA), a new class of practical adversarial attacks against pervasive camera-based authentication systems, where a…
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…
Universal Anti-forensics Attack against Image Forgery Detection via Multi-modal Guidance
Haipeng Li, Rongxuan Peng, Anwei Luo +3
The rapid advancement of AI-Generated Content (AIGC) technologies poses significant challenges for authenticity assessment. However, existing evaluation protocols largely overlook…
FS-IQA: Certified Feature Smoothing for Robust Image Quality Assessment
Ekaterina Shumitskaya, Dmitriy Vatolin, Anastasia Antsiferova
We propose a novel certified defense method for Image Quality Assessment (IQA) models based on randomized smoothing with noise applied in the feature space rather than the input sp…
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…
Robustness as Architecture: Designing IQA Models to Withstand Adversarial Perturbations
Igor Meleshin, Anna Chistyakova, Anastasia Antsiferova +1
Image Quality Assessment (IQA) models are increasingly relied upon to evaluate image quality in real-world systems -- from compression and enhancement to generation and streaming.…
NIC-RobustBench: A Comprehensive Open-Source Toolkit for Neural Image Compression and Robustness Analysis
Georgii Bychkov, Khaled Abud, Egor Kovalev +4
Neural image compression (NIC) is increasingly used in computer vision pipelines, as learning-based models are able to surpass traditional algorithms in compression efficiency. How…
Watermark Overwriting Attack on StegaStamp algorithm
I. F. Serzhenko, L. A. Khaertdinova, M. A. Pautov +1
This paper presents an attack method on the StegaStamp watermarking algorithm that completely removes watermarks from an image with minimal quality loss, developed as part of the N…
Accelerated zero-order SGD under high-order smoothness and overparameterized regime
Georgii Bychkov, Darina Dvinskikh, Anastasia Antsiferova +2
We present a novel gradient-free algorithm to solve a convex stochastic optimization problem, such as those encountered in medicine, physics, and machine learning (e.g., adversaria…
Stochastic BIQA: Median Randomized Smoothing for Certified Blind Image Quality Assessment
Ekaterina Shumitskaya, Mikhail Pautov, Dmitriy Vatolin +1
Most modern No-Reference Image-Quality Assessment (NR-IQA) metrics are based on neural networks vulnerable to adversarial attacks. Attacks on such metrics lead to incorrect image/v…
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…
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…
Ti-Patch: Tiled Physical Adversarial Patch for no-reference video quality metrics
Victoria Leonenkova, Ekaterina Shumitskaya, Anastasia Antsiferova +1
Objective no-reference image- and video-quality metrics are crucial in many computer vision tasks. However, state-of-the-art no-reference metrics have become learning-based and are…
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…
IOI: Invisible One-Iteration Adversarial Attack on No-Reference Image- and Video-Quality Metrics
Ekaterina Shumitskaya, Anastasia Antsiferova, Dmitriy Vatolin
No-reference image- and video-quality metrics are widely used in video processing benchmarks. The robustness of learning-based metrics under video attacks has not been widely studi…