6 papers
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…
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…
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…
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…
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…