5 papers
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…