Conference Paper

Forensic estimation of gamma correction in digital images

Inst. of Inf. Sci., Beijing Jiaotong Univ., Beijing, China
DOI: 10.1109/ICIP.2010.5652701 Conference: Image Processing (ICIP), 2010 17th IEEE International Conference on
Source: IEEE Xplore


In the digital era, digital photographs become pervasive and are frequently used to record event facts. Authenticity and integrity of such photos can be ascertained by discovering more information about the previously applied operations. In this paper, we propose a forensic scheme for identifying and reconstructing gamma correction operations in digital images. Statistical abnormity on image grayscale histograms, which is caused by the contrast enhancement, is analyzed theoretically and measured effectively. Graylevel mapping functions involved in gamma correction can be estimated blindly. Experiments both on globally and locally applied corrected images show the validity of our proposed gamma estimation algorithm.

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Available from: Gang Cao, Jan 25, 2016
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    • "In [13], the authors estimate whether an image is contrast enhanced and reconstruct the original image. Other histogram based CE detectors involve [14] and [15]. A lot of anti-forensic strategies have been proposed against such first order statistics based CE detectors. "
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    ABSTRACT: Detecting Contrast Enhancement (CE) in images and anti-forensic approaches against such detectors have gained much attention in multimedia forensics lately. Several contrast enhancement detectors analyze the first order statistics such as gray-level histogram of images to determine whether an image is CE or not. In order to counter these detectors various anti-forensic techniques have been proposed. This led to a technique that utilized second order statistics of images for CE detection. In this paper, we propose an effective anti-forensic approach that performs CE without significant distortion in both the first and second order statistics of the enhanced image. We formulate an optimization problem using a variant of the well known Total Variation (TV) norm image restoration formulation. Experiments show that the algorithm effectively overcomes the first and second order statistics based detectors without loss in quality of the enhanced image.
    Full-text · Article · Dec 2015 · IEEE Signal Processing Letters
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    • "Let us also remark that none of the single-image methods, neither the blind gamma estimation [14], [15], [16], nor the CRF estimation ones [10], [17], are capable of improving their performance if extra images are available. III. "
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    ABSTRACT: Blind gamma estimation is the problem of estimating the gamma function that is applied to a linear image both for perceptual reasons and for the compensation of the non-linear behavior of displays. Gamma values change both inter- and intra-camera. In the latter case, the change comes from the use of different scene settings. In this letter we propose a new approach that relies on the use of more than a single image from the same scene. We estimate the gammas for all the different images at the same time with a method based on exploiting the structure of the standard in-camera processing pipeline. Our results improve over the state-of-the-art.
    Full-text · Article · Sep 2015 · IEEE Signal Processing Letters
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    • "The prior methods [10]–[12] fail to detect contrast enhancement in the previously middle/low quality JPEG-compressed images. To investigate the reasons behind such ineffectiveness, the impact of JPEG compression on histograms is analyzed. "
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    ABSTRACT: As a retouching manipulation, contrast enhancement is typically used to adjust the global brightness and contrast of digital images. Malicious users may also perform contrast enhancement locally for creating a realistic composite image. As such it is significant to detect contrast enhancement blindly for verifying the originality and authenticity of the digital images. In this paper, we propose two novel algorithms to detect the contrast enhancement involved manipulations in digital images. First, we focus on the detection of global contrast enhancement applied to the previously JPEG-compressed images, which are widespread in real applications. The histogram peak/gap artifacts incurred by the JPEG compression and pixel value mappings are analyzed theoretically, and distinguished by identifying the zero-height gap fingerprints. Second, we propose to identify the composite image created by enforcing contrast adjustment on either one or both source regions. The positions of detected blockwise peak/gap bins are clustered for recognizing the contrast enhancement mappings applied to different source regions. The consistency between regional artifacts is checked for discovering the image forgeries and locating the composition boundary. Extensive experiments have verified the effectiveness and efficacy of the proposed techniques.
    Full-text · Article · Mar 2014 · IEEE Transactions on Information Forensics and Security
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