13 citations · 45 across the 34 of their papers we have counts for
5 papers · 2 filters
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 Video Super-Resolution Quality Assessment: Methods and Results
Ivan Molodetskikh, Artem Borisov, Dmitriy Vatolin +24
This paper presents the Video Super-Resolution (SR) Quality Assessment (QA) Challenge that was part of the Advances in Image Manipulation (AIM) workshop, held in conjunction with E…
Can No-Reference Quality-Assessment Methods Serve as Perceptual Losses for Super-Resolution?
Egor Kashkarov, Egor Chistov, Ivan Molodetskikh +1
Perceptual losses play an important role in constructing deep-neural-network-based methods by increasing the naturalness and realism of processed images and videos. Use of perceptu…
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…