6 papers
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…
Towards properties of adversarial image perturbations
Egor Kuznetsov, Kirill Aistov, Maxim Koroteev
Using stochastic gradient approach we study the properties of adversarial perturbations resulting in noticeable growth of VMAF image quality metric. The structure of the perturbati…
VMAF Re-implementation on PyTorch: Some Experimental Results
Kirill Aistov, Maxim Koroteev
Based on the standard VMAF implementation we propose an implementation of VMAF using PyTorch framework. For this implementation comparisons with the standard (libvmaf) show the dis…
Image quality prediction using synthetic and natural codebooks: comparative results
Maxim Koroteev, Kirill Aistov, Valeriy Berezovskiy +1
We investigate a model for image/video quality assessment based on building a set of codevectors representing in a sense some basic properties of images, similar to well-known CORN…
On scale-invariant properties in natural images and their simulations
Maxim Koroteev, Kirill Aistov
We study samples of natural images for which a set of statistical characteristics is computed and scale-invariant properties of samples are demonstrated computationally. Computatio…