Dynamic Routing between Multimodal Capsules for Deepfake Image Editing Detection

State-of-the-art methods have demonstrated exceptional performance in detecting deepfakes, particularly when the entire image content is generated by text-to-image generation models. However, significant challenges remain in the cases of instructional image editing, where AI models conditionally generate content based on both a real image and an edit prompt. In such scenarios, the generated content closely resembles the original image, making it more difficult for detection systems to identify deepfakes. In this paper, we propose a novel multimodal capsule network designed to address the detection of deepfake image editing. Specifically, low-level capsules from multiple modalities are integrated to predict capsules in subsequent layers. High-level capsules compete to select relevant low-level capsules, effectively aggregating local features to detect manipulated entities. The proposed approach is evaluated on diverse datasets, including natural images from real-world scenarios. Experimental results demonstrate that our model significantly outperforms state-of-the-art methods, achieving a substantial 10% improvement in Three-turn Edits or 20% improvement in Open Images Edits. Ablation studies further validate the robustness of the network, achieving detection rates exceeding 94% against natural perturbations and over 91% recall against white-box and black-box attacks. Additionally, the model effectively adapts to unseen image editing datasets, highlighting its ability to generalize across diverse and previously unencountered editing scenarios.