Zero-Shot Color Image Manipulation Localization via Noise Residual Artifact Pattern Analysis
- URL: http://arxiv.org/abs/2608.20558v1
- Date: Thu, 20 Aug 2026 20:37:21 GMT
- Title: Zero-Shot Color Image Manipulation Localization via Noise Residual Artifact Pattern Analysis
- Abstract summary: This work proposes a zero-shot, training-free blind image manipulation localization pipeline.<n>It estimates a reference artifact pattern directly from the noise residual of a single suspect image.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Digital cameras embed device-specific artifacts into every acquired image through demosaicing, in-camera post-processing, and lossy compression. These traces constitute a forensic signal that can be exploited to assess image authenticity. Existing passive methods rely predominantly on the green channel of the Bayer residual, discarding the correlated information available in the remaining color channels and typically requiring training data or device enrollment. This work proposes a zero-shot, training-free blind image manipulation localization pipeline that estimates a reference artifact pattern directly from the noise residual of a single suspect image, without assuming a fixed filter configuration, color layout, or block period. The pipeline incorporates a principled denoiser selection criterion based on the acquired-to-interpolated noise variance ratio, a block-level correlation analysis against the estimated reference pattern, and a two-component Gaussian Mixture Model scoring stage that produces a pixel-level tampering probability map. An ablation study evaluates the impact of denoiser choice and block size on localization accuracy, and comparisons against state-of-the-art passive methods demonstrate the competitiveness of the proposed zero-shot approach.
Related papers
- Efficient and Robust Remote Sensing Image Denoising Using Randomized Approximation of Geodesics' Gramian on the Manifold Underlying the Patch Space [2.56711111236449]
We present a robust remote sensing image denoising method that doesn't require additional training samples.<n>The method asserts a unique emphasis on each color channel during denoising so the three denoised channels are merged to produce the final image.
arXiv Detail & Related papers (2025-04-15T02:46:05Z) - Divide and Conquer: Heterogeneous Noise Integration for Diffusion-based Adversarial Purification [75.09791002021947]
Existing purification methods aim to disrupt adversarial perturbations by introducing a certain amount of noise through a forward diffusion process, followed by a reverse process to recover clean examples.<n>This approach is fundamentally flawed as the uniform operation of the forward process compromises normal pixels while attempting to combat adversarial perturbations.<n>We propose a heterogeneous purification strategy grounded in the interpretability of neural networks.<n>Our method decisively applies higher-intensity noise to specific pixels that the target model focuses on while the remaining pixels are subjected to only low-intensity noise.
arXiv Detail & Related papers (2025-03-03T11:00:25Z) - The Crystal Ball Hypothesis in diffusion models: Anticipating object positions from initial noise [92.53724347718173]
Diffusion models have achieved remarkable success in text-to-image generation tasks.
We identify specific regions within the initial noise image, termed trigger patches, that play a key role for object generation in the resulting images.
arXiv Detail & Related papers (2024-06-04T05:06:00Z) - ASCNet: Asymmetric Sampling Correction Network for Infrared Image Destriping [25.816984064145906]
We propose a novel infrared image destriping method called Asymmetric Sampling Correction Network (ASCNet)<n>Our ASCNet consists of three core elements: Residual Haar Discrete Wavelet Transform (RHDWT), Pixel Shuffle (PS), and Column Non-uniformity Correction Module (CNCM)
arXiv Detail & Related papers (2024-01-28T06:23:55Z) - RANRAC: Robust Neural Scene Representations via Random Ray Consensus [12.161889666145127]
RANdom RAy Consensus (RANRAC) is an efficient approach to eliminate the effect of inconsistent data.
We formulate a fuzzy adaption of the RANSAC paradigm, enabling its application to large scale models.
Results indicate significant improvements compared to state-of-the-art robust methods for novel-view synthesis.
arXiv Detail & Related papers (2023-12-15T13:33:09Z) - Pixel-Inconsistency Modeling for Image Manipulation Localization [59.968362815126326]
Digital image forensics plays a crucial role in image authentication and manipulation localization.
This paper presents a generalized and robust manipulation localization model through the analysis of pixel inconsistency artifacts.
Experiments show that our method successfully extracts inherent pixel-inconsistency forgery fingerprints.
arXiv Detail & Related papers (2023-09-30T02:54:51Z) - Gradpaint: Gradient-Guided Inpainting with Diffusion Models [71.47496445507862]
Denoising Diffusion Probabilistic Models (DDPMs) have recently achieved remarkable results in conditional and unconditional image generation.
We present GradPaint, which steers the generation towards a globally coherent image.
We generalizes well to diffusion models trained on various datasets, improving upon current state-of-the-art supervised and unsupervised methods.
arXiv Detail & Related papers (2023-09-18T09:36:24Z) - Anomaly Detection with Conditioned Denoising Diffusion Models [32.37548329437798]
We introduce Denoising Diffusion Anomaly Detection (DDAD), a novel denoising process for image reconstruction conditioned on a target image.
Our anomaly detection framework employs the conditioning mechanism, where the target image is set as the input image to guide the denoising process.
DDAD achieves state-of-the-art results of (99.8 %) and (98.9 %) image-level AUROC respectively.
arXiv Detail & Related papers (2023-05-25T11:54:58Z) - Poisson2Sparse: Self-Supervised Poisson Denoising From a Single Image [34.27748767631027]
We present a novel self-supervised learning method for single-image denoising.
We approximate traditional iterative optimization algorithms for image denoising with a recurrent neural network.
Our method outperforms the state-of-the-art approaches in terms of PSNR and SSIM.
arXiv Detail & Related papers (2022-06-04T00:08:58Z) - Treatment Learning Causal Transformer for Noisy Image Classification [62.639851972495094]
In this work, we incorporate this binary information of "existence of noise" as treatment into image classification tasks to improve prediction accuracy.
Motivated from causal variational inference, we propose a transformer-based architecture, that uses a latent generative model to estimate robust feature representations for noise image classification.
We also create new noisy image datasets incorporating a wide range of noise factors for performance benchmarking.
arXiv Detail & Related papers (2022-03-29T13:07:53Z) - Deblurring via Stochastic Refinement [85.42730934561101]
We present an alternative framework for blind deblurring based on conditional diffusion models.
Our method is competitive in terms of distortion metrics such as PSNR.
arXiv Detail & Related papers (2021-12-05T04:36:09Z) - Salt and pepper noise removal method based on stationary Framelet
transform with non-convex sparsity regularization [1.101002667958165]
Salt and pepper noise removal is an inverse problem in image processing, and it aims to restore image information with high quality.
Traditional salt and pepper denoising methods have two limitations.
arXiv Detail & Related papers (2021-10-18T09:07:31Z)
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.