Exploring Weaknesses of Generative Image Watermarks against Latent Frequency Masking

Kirill Aistov; Khaled Abud; Irina Serzhenko; Egor Kovalev; Aleksey Yakushev; Aleksandr Akimenkov; Dmitry Obydenkov; Yury Markin; Sergey Lavrushkin; Dmitriy Vatolin; Anastasia Antsiferova

arXiv · 2610.02010

Exploring Weaknesses of Generative Image Watermarks against Latent Frequency Masking

Kirill Aistov·Khaled Abud·Irina Serzhenko·Egor Kovalev·Aleksey Yakushev·Aleksandr Akimenkov·Dmitry Obydenkov·Yury Markin·Sergey Lavrushkin·Dmitriy Vatolin·Anastasia Antsiferova

View original source ↗Read source document ↗Export metadata ↓

Abstract

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 introduce Latent Frequency Masking, an attack that erases watermark evidence by replacing selected Fourier coefficients in the latent representation of a watermarked image. The replacement can be sampled from Gaussian noise for efficiency or derived from diffusion regeneration for improved image preservation. We provide a theoretical distortion bound relating the change between the reconstructed adversarial image and the masked latent-frequency perturbation. We evaluate the proposed attack against six diffusion watermarking methods on images generated from DiffusionDB and MS-COCO prompts. Latent Frequency Masking removes or substantially weakens several watermarks while preserving perceptual quality and achieving favorable runtime compared with existing attacks. These results identify latent-frequency manipulation as a practical attack surface and highlight the need to include such attacks in robustness evaluations of generative image watermarking.

Explore related subjects

cs.CV cs.AI cs.MM

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Kirill Aistov, Khaled Abud, Irina Serzhenko, Egor Kovalev, Aleksey Yakushev, Aleksandr Akimenkov, Dmitry Obydenkov, Yury Markin, Sergey Lavrushkin, Dmitriy Vatolin, Anastasia Antsiferova. 2026-10-01. Exploring Weaknesses of Generative Image Watermarks against Latent Frequency Masking. https://arxiv.org/abs/2610.02010

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

MaPa: Text-driven Photorealistic Material Painting for 3D Shapes

This paper aims to generate materials for 3D meshes from text descriptions. Unlike existing methods that synthesize texture maps, we propose to generate segment-wise procedural material graphs as the appearance representation, which supports high-quality rendering and provides substantial flexibility in editing. Instead of relying on extensive paired data, i.e., 3D meshes with material graphs and corresponding text descriptions, to train a material graph generative model, we propose to leverage the pre-trained 2D diffusion model as a bridge to connect the text and material graphs. Specifically, our approach decomposes a shape into a set of segments and designs a segment-controlled diffusion model to synthesize 2D images that are aligned with mesh parts. Based on generated images, we initialize parameters of material graphs and fine-tune them through the differentiable rendering module to produce materials in accordance with the textual description. Extensive experiments demonstrate the superior performance of our framework in photorealism, resolution, and editability over existing methods. Project page: https://zju3dv.github.io/MaPa

cs.CV↗

arXiv2026

REVEAL: Robust Evolution of Vision-Language Models for Explainable AI-Video Detection

The rapid advancement of AI-generated video poses challenges to digital authenticity and security. Current detection methods, often trained on specific datasets, struggle with the ever-evolving landscape of generative techniques and unseen manipulations. We introduce a framework leveraging Vision Language Models (VLMs) for robust AI-generated video detection. Our approach equips the VLM with the ability to reason about video content and use external tools to identify subtle inconsistencies, mirroring human system 2 thinking. Our self-evolving VLM dynamically selects and composes appropriate tools, enhancing its ability to generalize to novel video generation techniques. The modular design promotes interpretability, allowing for a clearer understanding of VLM's decision-making process. To evaluate, we establish the first benchmark VidForensic containing 1.4k+ high-quality AI-generated videos across eight generative models. Experiments show that REVEAL improves F1 scores by 9.1% to 30.2% over top baselines across our datasets for VLMs, notably for GPT-4o, Gemini 1.5 pro, and QWen-VL-Max, and Llava-One-Vision-7B. While open-world AI-video detection remains an open challenge, our results indicate that existing methods fail primarily because they lack tool-enabled, higher-order reasoning.

cs.CV↗

arXiv2026

What Drives Compositional Generalization in Visual Generative Models? The Importance of Continuous Training Objectives

Compositional generalization, the ability to generate novel combinations of known concepts, is a key ingredient for visual generative models. Yet, not all mechanisms that enable or inhibit it are fully understood. In this work, we conduct a systematic study of which design choices critically determine compositional generalization in image and video generation. By isolating independent design axes, we identify two key factors strongly associated with compositional success: (i) whether the training objective operates on a discrete or continuous distribution, and (ii) the completeness of conditioning information about constituent factors during training. We also show that relaxing the discrete loss with an auxiliary continuous latent objective can partially recover compositional performance in discrete models like MaskGIT. Our findings, corroborated by diverse compositional tasks and preliminary evidence in world models and LLMs, motivate a shift toward continuous objectives for compositional generalization.

cs.CV↗