Learning Feature Disentanglement and Dynamic Fusion for Recaptured Image Forensic

• URL: http://arxiv.org/abs/2206.06103v1
• Date: Mon, 13 Jun 2022 12:47:13 GMT
• Title: Learning Feature Disentanglement and Dynamic Fusion for Recaptured Image Forensic
• Authors: Shuyu Miao, Lin Zheng, Hong Jin
• Abstract summary: We explicitly redefine image recapture forensic task as four patterns of image recapture recognition, i.e., moire recapture, edge recapture, artifact recapture, and other recapture. We propose a novel Feature Disentanglement and Dynamic Fusion (FDDF) model to adaptively learn the most effective recapture representation for covering different recapture pattern recognition. To the best of our knowledge, we are the first to propose a general model and a general real-scene large-scale dataset for recaptured image forensic.
• Score: 7.820667552233989
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Image recapture seriously breaks the fairness of artificial intelligent (AI) systems, which deceives the system by recapturing others' images. Most of the existing recapture models can only address a single pattern of recapture (e.g., moire, edge, artifact, and others) based on the datasets with simulated recaptured images using fixed electronic devices. In this paper, we explicitly redefine image recapture forensic task as four patterns of image recapture recognition, i.e., moire recapture, edge recapture, artifact recapture, and other recapture. Meanwhile, we propose a novel Feature Disentanglement and Dynamic Fusion (FDDF) model to adaptively learn the most effective recapture feature representation for covering different recapture pattern recognition. Furthermore, we collect a large-scale Real-scene Universal Recapture (RUR) dataset containing various recapture patterns, which is about five times the number of previously published datasets. To the best of our knowledge, we are the first to propose a general model and a general real-scene large-scale dataset for recaptured image forensic. Extensive experiments show that our proposed FDDF can achieve state-of-the-art performance on the RUR dataset.

Related papers

• A Survey on All-in-One Image Restoration: Taxonomy, Evaluation and Future Trends [67.43992456058541]
  Image restoration (IR) refers to the process of improving visual quality of images while removing degradation, such as noise, blur, weather effects, and so on. Traditional IR methods typically target specific types of degradation, which limits their effectiveness in real-world scenarios with complex distortions. The all-in-one image restoration (AiOIR) paradigm has emerged, offering a unified framework that adeptly addresses multiple degradation types.
  arXiv  Detail & Related papers  (2024-10-19T11:11:09Z)
• HyperDet: Generalizable Detection of Synthesized Images by Generating and Merging A Mixture of Hyper LoRAs [17.88153857572688]
  We introduce a novel and generalizable detection framework termed HyperDet. In this work, we propose a novel objective function that balances the pixel and semantic artifacts effectively. Our work paves a new way to establish generalizable domain-specific fake image detectors based on pretrained large vision models.
  arXiv  Detail & Related papers  (2024-10-08T13:43:01Z)
• Zero-Shot Detection of AI-Generated Images [54.01282123570917]
  We propose a zero-shot entropy-based detector (ZED) to detect AI-generated images. Inspired by recent works on machine-generated text detection, our idea is to measure how surprising the image under analysis is compared to a model of real images. ZED achieves an average improvement of more than 3% over the SoTA in terms of accuracy.
  arXiv  Detail & Related papers  (2024-09-24T08:46:13Z)
• Rethinking Image Super-Resolution from Training Data Perspectives [54.28824316574355]
  We investigate the understudied effect of the training data used for image super-resolution (SR) With this, we propose an automated image evaluation pipeline. We find that datasets with (i) low compression artifacts, (ii) high within-image diversity as judged by the number of different objects, and (iii) a large number of images from ImageNet or PASS all positively affect SR performance.
  arXiv  Detail & Related papers  (2024-09-01T16:25:04Z)
• On quantifying and improving realism of images generated with diffusion [50.37578424163951]
  We propose a metric, called Image Realism Score (IRS), computed from five statistical measures of a given image. IRS is easily usable as a measure to classify a given image as real or fake. We experimentally establish the model- and data-agnostic nature of the proposed IRS by successfully detecting fake images generated by Stable Diffusion Model (SDM), Dalle2, Midjourney and BigGAN. Our efforts have also led to Gen-100 dataset, which provides 1,000 samples for 100 classes generated by four high-quality models.
  arXiv  Detail & Related papers  (2023-09-26T08:32:55Z)
• Towards Real-World Burst Image Super-Resolution: Benchmark and Method [93.73429028287038]
  In this paper, we establish a large-scale real-world burst super-resolution dataset, i.e., RealBSR, to explore the faithful reconstruction of image details from multiple frames. We also introduce a Federated Burst Affinity network (FBAnet) to investigate non-trivial pixel-wise displacement among images under real-world image degradation.
  arXiv  Detail & Related papers  (2023-09-09T14:11:37Z)
• RBSR: Efficient and Flexible Recurrent Network for Burst Super-Resolution [57.98314517861539]
  Burst super-resolution (BurstSR) aims at reconstructing a high-resolution (HR) image from a sequence of low-resolution (LR) and noisy images. In this paper, we suggest fusing cues frame-by-frame with an efficient and flexible recurrent network.
  arXiv  Detail & Related papers  (2023-06-30T12:14:13Z)
• Understanding invariance via feedforward inversion of discriminatively trained classifiers [30.23199531528357]
  Past research has discovered that some extraneous visual detail remains in the output logits. We develop a feedforward inversion model that produces remarkably high fidelity reconstructions. Our approach is based on BigGAN, with conditioning on logits instead of one-hot class labels.
  arXiv  Detail & Related papers  (2021-03-15T17:56:06Z)
• Bayesian Image Reconstruction using Deep Generative Models [7.012708932320081]
  In this work, we leverage state-of-the-art (SOTA) generative models for building powerful image priors. Our method, called Bayesian Reconstruction through Generative Models (BRGM), uses a single pre-trained generator model to solve different image restoration tasks.
  arXiv  Detail & Related papers  (2020-12-08T17:11:26Z)

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.