AWPD: Frequency Shield Network for Agnostic Watermark Presence Detection

TL;DR

This paper introduces AWPD, a new task for detecting the presence of invisible watermarks in images without prior knowledge, and proposes FSNet, a model that leverages frequency domain features for improved detection.

Contribution

The paper presents a novel agnostic watermark detection task, constructs a large-scale dataset, and develops FSNet, a frequency-aware model with adaptive spectral perception and multi-spectral attention.

Findings

FSNet outperforms baseline models in zero-shot watermark detection.

The UniFreq-100K dataset supports diverse watermark algorithms.

FSNet effectively amplifies watermark signals and suppresses semantics.

Abstract

Invisible watermarks, as an essential technology for image copyright protection, have been widely deployed with the rapid development of social media and AIGC. However, existing invisible watermark detection heavily relies on prior knowledge of specific algorithms, leading to limited detection capabilities for ``unknown watermarks'' in open environments. To this end, we propose a novel task named Agnostic Watermark Presence Detection (AWPD), which aims to identify whether an image carries a copyright mark without requiring decoding information. We construct the UniFreq-100K dataset, comprising large-scale samples across various invisible watermark embedding algorithms. Furthermore, we propose the Frequency Shield Network (FSNet). This model deploys an Adaptive Spectral Perception Module (ASPM) in the shallow layers, utilizing learnable frequency gating to dynamically amplify high-frequency watermark signals while suppressing low-frequency semantics. In the deep layers, the network introduces Dynamic Multi-Spectral Attention (DMSA) combined with tri-stream extremum pooling to deeply mine watermark energy anomalies, forcing the model to precisely focus on sensitive frequency bands. Extensive experiments demonstrate that FSNet exhibits superior zero-shot detection capabilities on the AWPD task, outperforming existing baseline models. Code and datasets will be released upon acceptance.