Diffusion-based models have recently revolutionized image generation, achieving unprecedented levels of fidelity. However, consistent generation of high-quality images remains challenging partly due to the lack of conditioning mechanisms for perceptual quality. In this work, we propose methods to integrate image quality assessment (IQA) models into diffusion-based generators, enabling quality-aware image generation. We show that diffusion models can learn complex qualitative relationships from both IQA models' outputs and internal activations. First, we experiment with gradient-based guidance to optimize image quality directly and show this method has limited generalizability. To address this, we introduce IQA-Adapter, a novel framework that conditions generation on target quality levels by learning the implicit relationship between images and quality scores. When conditioned on high target quality, IQA-Adapter can shift the distribution of generated images towards a higher-quality subdomain, and, inversely, it can be used as a degradation model, generating progressively more distorted images when provided with a lower-quality signal. Under high-quality condition, IQA-Adapter achieves up to a 10% improvement across multiple objective metrics, as confirmed by a user preference study, while preserving generative diversity and content. Furthermore, we extend IQA-Adapter to a reference-based conditioning scenario, utilizing the rich activation space of IQA models to transfer highly specific, content-agnostic qualitative features between images.

Modern neural-network-based Image Quality Assessment (IQA) metrics are vulnerable to adversarial attacks, which can be exploited to manipulate search engine rankings, benchmark results, and content quality assessments, raising concerns about the reliability of IQA metrics in critical applications. This paper presents the first comprehensive study of IQA defense mechanisms in response to adversarial attacks on these metrics to pave the way for safer use of IQA metrics. We systematically evaluated 30 defense strategies, including purification, training-based, and certified methods --- and applied 14 adversarial attacks in adaptive and non-adaptive settings to compare these defenses on 9 no-reference IQA metrics. Our proposed benchmark aims to guide the development of IQA defense methods and is open to submissions; the latest results and code are at https://msu-video-group.github.io/adversarial-defenses-for-iqa/.

As invisible image watermarking gains importance for verifying AI-generated content, consistency and reproducibility remain major challenges due to the diverse methods, datasets, attacks, and metrics.We aim to provide a flexible, extensible, and user-friendly framework that enables systematic testing of watermarking methods under various conditions.We developed WIBE, a framework with command-line interfaces and YAML configuration support, enabling users to evaluate a wide range of image watermarking algorithms on various datasets, apply configurable attack scenarios, and compute standard performance metrics. WIBE includes a library of pre-implemented methods and supports integration of new watermarking techniques, attacks, metrics, and datasets through a plugin-based architecture.WIBE enables rapid prototyping, reproducible experiments, and insightful comparison of watermarking robustness. In our demo, we present its core features, plugin extensibility, and interactive infographics, making it a practical tool for researchers and practitioners working at the intersection of AI and media integrity.Project on GitHub: https://github.com/ispras/wibeYouTube video: https://youtu.be/lbWWB1crrwk

Nowadays, neural-network-based image- and video-quality metrics perform better than traditional methods. However, they also became more vulnerable to adversarial attacks that increase metrics' scores without improving visual quality. The existing benchmarks of quality metrics compare their performance in terms of correlation with subjective quality and calculation time. Nonetheless, the adversarial robustness of image-quality metrics is also an area worth researching. This paper analyses modern metrics' robustness to different adversarial attacks. We adapted adversarial attacks from computer vision tasks and compared attacks' efficiency against 15 no-reference image- and video-quality metrics. Some metrics showed high resistance to adversarial attacks, which makes their usage in benchmarks safer than vulnerable metrics. The benchmark accepts submissions of new metrics for researchers who want to make their metrics more robust to attacks or to find such metrics for their needs. The latest results can be found online: https://videoprocessing.ai/benchmarks/metrics-robustness.html.