Detecting doubly compressed images based on quantization noise model and image restoration

2009 ◽  
Author(s):  
Yi-Lei Chen ◽  
Chiou-Ting Hsu
Keyword(s):  
Image Restoration ◽  
Quantization Noise ◽  
Noise Model ◽  
Compressed Images

scholarly journals Forgery Detection in Digital Images by Multi-Scale Noise Estimation

2021 ◽  
Vol 7 (7) ◽  
pp. 119
Author(s):  
Marina Gardella ◽  
Pablo Musé ◽  
Jean-Michel Morel ◽  
Miguel Colom
Keyword(s):  
Noise Model ◽  
Correlated Noise ◽  
Forgery Detection ◽  
Color Channel ◽  
Image Block ◽  
Multi Scale ◽  
Compressed Images ◽  
Inconsistency Analysis ◽  
Highly Correlated ◽  
The Moment

A complex processing chain is applied from the moment a raw image is acquired until the final image is obtained. This process transforms the originally Poisson-distributed noise into a complex noise model. Noise inconsistency analysis is a rich source for forgery detection, as forged regions have likely undergone a different processing pipeline or out-camera processing. We propose a multi-scale approach, which is shown to be suitable for analyzing the highly correlated noise present in JPEG-compressed images. We estimate a noise curve for each image block, in each color channel and at each scale. We then compare each noise curve to its corresponding noise curve obtained from the whole image by counting the percentage of bins of the local noise curve that are below the global one. This procedure yields crucial detection cues since many forgeries create a local noise deficit. Our method is shown to be competitive with the state of the art. It outperforms all other methods when evaluated using the MCC score, or on forged regions large enough and for colorization attacks, regardless of the evaluation metric.

Tampering Localization in Double Compressed Images by Investigating Noise Quantization

2016 ◽  
Vol 8 (3) ◽  
pp. 46-62
Author(s):  
Archana Vasant Mire ◽  
Sanjay B. Dhok ◽  
Naresh J. Mistry ◽  
Prakash D. Porey
Keyword(s):  
Digital Images ◽  
Forensic Analysis ◽  
Experimental Results ◽  
Quantization Noise ◽  
Svm Classifier ◽  
Compression Process ◽  
Image Tampering ◽  
Compressed Images ◽  
Discrepancy Model ◽  
Uniform Quantization

Noise is uniformly distributed throughout an untampered image. Tampering operations destroy this uniformity and introduce inconsistency in the tampered region. Hence, noise discrepancy is often investigated in forensic analysis of uncompressed digital images. However, noise in compressed images has got very little attention from the forensic experts. The JPEG compression process itself introduces uniform quantization noise throughout an image, making this investigation difficult. In this paper, the authors have proposed a new noise compression discrepancy model, which blindly estimates this discrepancy in the compressed images. Considering the smaller tampered region, SVM classifier was trained using noise features of test sub-images and its nonaligned recompressed versions. Each of the test sub-images was further classified using this classifier. Experimental results show that in some cases, the proposed approach can achieve better performance compared with other JPEG artefact based techniques.

Learning-based image restoration for compressed images

2012 ◽  
Vol 27 (1) ◽  
pp. 54-65 ◽  
Author(s):  
Lin Ma ◽  
Debin Zhao ◽  
Wen Gao
Keyword(s):  
Image Restoration ◽  
Compressed Images

A Comparative Study of Image Deblurring Techniques

2020 ◽  
Vol 17 (9) ◽  
pp. 4571-4579
Author(s):  
Rajbir Singh ◽  
Sumit Bansal
Keyword(s):  
Comparative Study ◽  
Image Restoration ◽  
Digital Image ◽  
Minimum Mean Square Error ◽  
Direct Methods ◽  
Structural Similarity ◽  
Single Step ◽  
Noise Model ◽  
Image Deconvolution ◽  
Indirect Methods

The method of recovering a true image from degraded one, to analyze that digital image and characteristics with no artifact errors is known as Image Restoration. These techniques are of two types: direct methods and indirect methods. Direct methods are those in which the results of image restoration are produced in one single step. Indirect methods are those in which the results of image restoration are produced after various steps. This method is termed as blind image deconvolution, when the known info is just the blurred digital image and no info about the (Point Spread Function) (PSF) or the degrading model. The target of the procedure is to recover both the latent (un-blurred) image and the blur kernel, simultaneously. In this paper, we presented a comprehensive research of image noise model,de-blurring methods, blur types, and a comparative study of various deblurring methods. We have implemented number experiments to study these methods according to their performance, (Peak Signal to Noise Ratio) PSNR, (structural similarity) SSIM, blur type, and (Minimum Mean Square Error) MMSE.

scholarly journals Spectral and Energy Efficiency of Distributed Massive MIMO with Low-Resolution ADC

Electronics ◽  
2018 ◽  
Vol 7 (12) ◽  
pp. 391 ◽  
Author(s):  
Jiamin Li ◽  
Qian Lv ◽  
Jing Yang ◽  
Pengcheng Zhu ◽  
Xiaohu You
Keyword(s):  
Energy Efficiency ◽  
Massive Mimo ◽  
Mimo Systems ◽  
Numerical Results ◽  
Quantization Noise ◽  
Noise Model ◽  
Low Resolution ◽  
Zero Forcing ◽  
Analog To Digital ◽  
Linear Receivers

In this paper, considering a more realistic case where the low-resolution analog-to-digital convertors (ADCs) are employed at receiver antennas, we investigate the spectral and energy efficiency in multi-cell multi-user distributed massive multi-input multi-output (MIMO) systems with two linear receivers. An additive quantization noise model is provided first to study the effects of quantization noise. Using the model provided, the closed-form expressions for the uplink achievable rates with a zero-forcing (ZF) receiver and a maximum ratio combination (MRC) receiver under quantization noise and pilot contamination are derived. Furthermore, the asymptotic achievable rates are also given when the number of quantization bits, the per user transmit power, and the number of antennas per remote antenna unit (RAU) go to infinity, respectively. Numerical results prove that the theoretical analysis is accurate and show that quantization noise degrades the performance in spectral efficiency, but the growth in the number of antennas can compensate for the degradation. Furthermore, low-resolution ADCs with 3 or 4 bits outperform perfect ADCs in energy efficiency. Numerical results imply that it is preferable to use low-resolution ADCs in distributed massive MIMO systems.

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