Reinforced Multi-teacher Knowledge Distillation for Efficient General Image Forgery Detection and Localization

• URL: http://arxiv.org/abs/2504.05224v1
• Date: Mon, 07 Apr 2025 16:12:05 GMT
• Title: Reinforced Multi-teacher Knowledge Distillation for Efficient General Image Forgery Detection and Localization
• Authors: Zeqin Yu, Jiangqun Ni, Jian Zhang, Haoyi Deng, Yuzhen Lin,
• Abstract summary: Image forgery detection and localization (IFDL) is of vital importance as forged images can spread misinformation that poses potential threats to our daily lives.<n>Previous methods still struggled to effectively handle forged images processed with diverse forgery operations in real-world scenarios.<n>We propose a novel Reinforced Multi-teacher Knowledge Distillation (Re-MTKD) framework for the IFDL task, structured around an encoder-decoder textbfConvNeXt-textbfUperNet.
• Score: 9.721443347546876
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Image forgery detection and localization (IFDL) is of vital importance as forged images can spread misinformation that poses potential threats to our daily lives. However, previous methods still struggled to effectively handle forged images processed with diverse forgery operations in real-world scenarios. In this paper, we propose a novel Reinforced Multi-teacher Knowledge Distillation (Re-MTKD) framework for the IFDL task, structured around an encoder-decoder \textbf{C}onvNeXt-\textbf{U}perNet along with \textbf{E}dge-Aware Module, named Cue-Net. First, three Cue-Net models are separately trained for the three main types of image forgeries, i.e., copy-move, splicing, and inpainting, which then serve as the multi-teacher models to train the target student model with Cue-Net through self-knowledge distillation. A Reinforced Dynamic Teacher Selection (Re-DTS) strategy is developed to dynamically assign weights to the involved teacher models, which facilitates specific knowledge transfer and enables the student model to effectively learn both the common and specific natures of diverse tampering traces. Extensive experiments demonstrate that, compared with other state-of-the-art methods, the proposed method achieves superior performance on several recently emerged datasets comprised of various kinds of image forgeries.

Related papers

• UniVG: A Generalist Diffusion Model for Unified Image Generation and Editing [59.590505989071175]
  Text-to-Image (T2I) diffusion models have shown impressive results in generating visually compelling images following user prompts. We introduce UniVG, a generalist diffusion model capable of supporting a diverse range of image generation tasks with a single set of weights.
  arXiv  Detail & Related papers  (2025-03-16T21:11:25Z)
• Is Contrastive Distillation Enough for Learning Comprehensive 3D Representations? [55.99654128127689]
  Cross-modal contrastive distillation has recently been explored for learning effective 3D representations.<n>Existing methods focus primarily on modality-shared features, neglecting the modality-specific features during the pre-training process.<n>We propose a new framework, namely CMCR, to address these shortcomings.
  arXiv  Detail & Related papers  (2024-12-12T06:09:49Z)
• ComKD-CLIP: Comprehensive Knowledge Distillation for Contrastive Language-Image Pre-traning Model [49.587821411012705]
  We propose ComKD-CLIP: Comprehensive Knowledge Distillation for Contrastive Language-Image Pre-traning Model. It distills the knowledge from a large teacher CLIP model into a smaller student model, ensuring comparable performance with significantly reduced parameters. EduAttention explores the cross-relationships between text features extracted by the teacher model and image features extracted by the student model.
  arXiv  Detail & Related papers  (2024-08-08T01:12:21Z)
• MOCA: Self-supervised Representation Learning by Predicting Masked Online Codebook Assignments [72.6405488990753]
  Self-supervised learning can be used for mitigating the greedy needs of Vision Transformer networks. We propose a single-stage and standalone method, MOCA, which unifies both desired properties. We achieve new state-of-the-art results on low-shot settings and strong experimental results in various evaluation protocols.
  arXiv  Detail & Related papers  (2023-07-18T15:46:20Z)
• Domain Generalization for Mammographic Image Analysis with Contrastive Learning [62.25104935889111]
  The training of an efficacious deep learning model requires large data with diverse styles and qualities. A novel contrastive learning is developed to equip the deep learning models with better style generalization capability. The proposed method has been evaluated extensively and rigorously with mammograms from various vendor style domains and several public datasets.
  arXiv  Detail & Related papers  (2023-04-20T11:40:21Z)
• Dense Depth Distillation with Out-of-Distribution Simulated Images [30.79756881887895]
  We study data-free knowledge distillation (KD) for monocular depth estimation (MDE) KD learns a lightweight model for real-world depth perception tasks by compressing it from a trained teacher model while lacking training data in the target domain. We show that our method outperforms the baseline KD by a good margin and even slightly better performance with as few as 1/6 of training images.
  arXiv  Detail & Related papers  (2022-08-26T07:10:01Z)
• Hand Image Understanding via Deep Multi-Task Learning [34.515382305252814]
  We propose a novel Hand Image Understanding (HIU) framework to extract comprehensive information of the hand object from a single RGB image. Our method significantly outperforms the state-of-the-art approaches on various widely-used datasets.
  arXiv  Detail & Related papers  (2021-07-24T16:28:06Z)
• Multimodal Contrastive Training for Visual Representation Learning [45.94662252627284]
  We develop an approach to learning visual representations that embraces multimodal data. Our method exploits intrinsic data properties within each modality and semantic information from cross-modal correlation simultaneously. By including multimodal training in a unified framework, our method can learn more powerful and generic visual features.
  arXiv  Detail & Related papers  (2021-04-26T19:23:36Z)
• Learning Deformable Image Registration from Optimization: Perspective, Modules, Bilevel Training and Beyond [62.730497582218284]
  We develop a new deep learning based framework to optimize a diffeomorphic model via multi-scale propagation. We conduct two groups of image registration experiments on 3D volume datasets including image-to-atlas registration on brain MRI data and image-to-image registration on liver CT data.
  arXiv  Detail & Related papers  (2020-04-30T03:23:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.