1 citations · 2 across the 7 of their papers we have counts for
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RandMark: On Random Watermarking of Visual Foundation Models
Anna Chistyakova, Mikhail Pautov
Being trained on large and diverse datasets, visual foundation models (VFMs) can be fine-tuned to achieve remarkable performance and efficiency in various downstream computer visio…
ActiveMark: on watermarking of visual foundation models via massive activations
Anna Chistyakova, Mikhail Pautov
Being trained on large and vast datasets, visual foundation models (VFMs) can be fine-tuned for diverse downstream tasks, achieving remarkable performance and efficiency in various…
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.…
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…
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…