Language-guided Hierarchical Fine-grained Image Forgery Detection and Localization

• URL: http://arxiv.org/abs/2410.23556v1
• Date: Thu, 31 Oct 2024 01:53:21 GMT
• Title: Language-guided Hierarchical Fine-grained Image Forgery Detection and Localization
• Authors: Xiao Guo, Xiaohong Liu, Iacopo Masi, Xiaoming Liu,
• Abstract summary: Differences in forgery attributes of images generated in CNN-synthesized and image-editing domains are large. We present a hierarchical fine-grained formulation for IFDL representation learning. As a result, the algorithm is encouraged to learn both comprehensive features and the inherent hierarchical nature of different forgery attributes.
• Score: 17.5445037141816
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Differences in forgery attributes of images generated in CNN-synthesized and image-editing domains are large, and such differences make a unified image forgery detection and localization (IFDL) challenging. To this end, we present a hierarchical fine-grained formulation for IFDL representation learning. Specifically, we first represent forgery attributes of a manipulated image with multiple labels at different levels. Then, we perform fine-grained classification at these levels using the hierarchical dependency between them. As a result, the algorithm is encouraged to learn both comprehensive features and the inherent hierarchical nature of different forgery attributes. In this work, we propose a Language-guided Hierarchical Fine-grained IFDL, denoted as HiFi-Net++. Specifically, HiFi-Net++ contains four components: a multi-branch feature extractor, a language-guided forgery localization enhancer, as well as classification and localization modules. Each branch of the multi-branch feature extractor learns to classify forgery attributes at one level, while localization and classification modules segment pixel-level forgery regions and detect image-level forgery, respectively. Also, the language-guided forgery localization enhancer (LFLE), containing image and text encoders learned by contrastive language-image pre-training (CLIP), is used to further enrich the IFDL representation. LFLE takes specifically designed texts and the given image as multi-modal inputs and then generates the visual embedding and manipulation score maps, which are used to further improve HiFi-Net++ manipulation localization performance. Lastly, we construct a hierarchical fine-grained dataset to facilitate our study. We demonstrate the effectiveness of our method on $8$ by using different benchmarks for both tasks of IFDL and forgery attribute classification. Our source code and dataset are available.

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