Arbitrary-sized Image Training and Residual Kernel Learning: Towards Image Fraud Identification

• URL: http://arxiv.org/abs/2005.11043v1
• Date: Fri, 22 May 2020 07:57:24 GMT
• Title: Arbitrary-sized Image Training and Residual Kernel Learning: Towards Image Fraud Identification
• Authors: Hongyu Li, Xiaogang Huang, Zhihui Fu, and Xiaolin Li
• Abstract summary: We propose a framework for training images of original input scales without resizing. Our arbitrary-sized image training method depends on the pseudo-batch gradient descent. With the learnt residual kernels and PBGD, the proposed framework achieved the state-of-the-art results in image fraud identification.
• Score: 10.47223719403823
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Preserving original noise residuals in images are critical to image fraud identification. Since the resizing operation during deep learning will damage the microstructures of image noise residuals, we propose a framework for directly training images of original input scales without resizing. Our arbitrary-sized image training method mainly depends on the pseudo-batch gradient descent (PBGD), which bridges the gap between the input batch and the update batch to assure that model updates can normally run for arbitrary-sized images. In addition, a 3-phase alternate training strategy is designed to learn optimal residual kernels for image fraud identification. With the learnt residual kernels and PBGD, the proposed framework achieved the state-of-the-art results in image fraud identification, especially for images with small tampered regions or unseen images with different tampering distributions.

Related papers

• Realistic Extreme Image Rescaling via Generative Latent Space Learning [51.85790402171696]
  We propose a novel framework called Latent Space Based Image Rescaling (LSBIR) for extreme image rescaling tasks. LSBIR effectively leverages powerful natural image priors learned by a pre-trained text-to-image diffusion model to generate realistic HR images. In the first stage, a pseudo-invertible encoder-decoder models the bidirectional mapping between the latent features of the HR image and the target-sized LR image. In the second stage, the reconstructed features from the first stage are refined by a pre-trained diffusion model to generate more faithful and visually pleasing details.
  arXiv  Detail & Related papers  (2024-08-17T09:51:42Z)
• Data Attribution for Text-to-Image Models by Unlearning Synthesized Images [71.23012718682634]
  The goal of data attribution for text-to-image models is to identify the training images that most influence the generation of a new image. We propose a new approach that efficiently identifies highly-influential images.
  arXiv  Detail & Related papers  (2024-06-13T17:59:44Z)
• Masked Image Training for Generalizable Deep Image Denoising [53.03126421917465]
  We present a novel approach to enhance the generalization performance of denoising networks. Our method involves masking random pixels of the input image and reconstructing the missing information during training. Our approach exhibits better generalization ability than other deep learning models and is directly applicable to real-world scenarios.
  arXiv  Detail & Related papers  (2023-03-23T09:33:44Z)
• Multiscale Structure Guided Diffusion for Image Deblurring [24.09642909404091]
  Diffusion Probabilistic Models (DPMs) have been employed for image deblurring. We introduce a simple yet effective multiscale structure guidance as an implicit bias. We demonstrate more robust deblurring results with fewer artifacts on unseen data.
  arXiv  Detail & Related papers  (2022-12-04T10:40:35Z)
• Fast Hybrid Image Retargeting [0.0]
  We propose a method that quantifies and limits warping distortions with the use of content-aware cropping. Our method outperforms recent approaches, while running in a fraction of their execution time.
  arXiv  Detail & Related papers  (2022-03-25T11:46:06Z)
• Unleashing the Potential of Unsupervised Pre-Training with Intra-Identity Regularization for Person Re-Identification [10.045028405219641]
  We design an Unsupervised Pre-training framework for ReID based on the contrastive learning (CL) pipeline, dubbed UP-ReID. We introduce an intra-identity (I$2$-)regularization in the UP-ReID, which is instantiated as two constraints coming from global image aspect and local patch aspect. Our UP-ReID pre-trained model can significantly benefit the downstream ReID fine-tuning and achieve state-of-the-art performance.
  arXiv  Detail & Related papers  (2021-12-01T07:16:37Z)
• Fidelity Estimation Improves Noisy-Image Classification with Pretrained Networks [12.814135905559992]
  We propose a method that can be applied on a pretrained classifier. Our method exploits a fidelity map estimate that is fused into the internal representations of the feature extractor. We show that when using our oracle fidelity map we even outperform the fully retrained methods, whether trained on noisy or restored images.
  arXiv  Detail & Related papers  (2021-06-01T17:58:32Z)
• A low-rank representation for unsupervised registration of medical images [10.499611180329804]
  We propose a novel approach based on a low-rank representation, i.e., Regnet-LRR, to tackle the problem of noisy data registration scenarios. We show that the low-rank representation can boost the ability and robustness of models as well as bring significant improvements in noisy data registration scenarios.
  arXiv  Detail & Related papers  (2021-05-20T07:04:10Z)
• Deep Variational Network Toward Blind Image Restoration [60.45350399661175]
  Blind image restoration is a common yet challenging problem in computer vision. We propose a novel blind image restoration method, aiming to integrate both the advantages of them. Experiments on two typical blind IR tasks, namely image denoising and super-resolution, demonstrate that the proposed method achieves superior performance over current state-of-the-arts.
  arXiv  Detail & Related papers  (2020-08-25T03:30:53Z)
• Distilling Localization for Self-Supervised Representation Learning [82.79808902674282]
  Contrastive learning has revolutionized unsupervised representation learning. Current contrastive models are ineffective at localizing the foreground object. We propose a data-driven approach for learning in variance to backgrounds.
  arXiv  Detail & Related papers  (2020-04-14T16:29:42Z)
• Exploiting Deep Generative Prior for Versatile Image Restoration and Manipulation [181.08127307338654]
  This work presents an effective way to exploit the image prior captured by a generative adversarial network (GAN) trained on large-scale natural images. The deep generative prior (DGP) provides compelling results to restore missing semantics, e.g., color, patch, resolution, of various degraded images.
  arXiv  Detail & Related papers  (2020-03-30T17:45:07Z)

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.