A Universal Attack Against Histogram-Based Image Forensics

2013 ◽  
Vol 5 (3) ◽  
pp. 35-52 ◽  
Author(s):  
Mauro Barni ◽  
Marco Fontani ◽  
Benedetta Tondi
Keyword(s):  
Statistical Model ◽  
Contrast Enhancement ◽  
Optimization Problem ◽  
Experimental Validation ◽  
Image Forensics ◽  
Perceptual Similarity ◽  
Image Histogram ◽  
Detection Algorithms ◽  
Splicing Detection

In this paper the authors propose a universal image counter-forensic scheme that contrasts any detector based on the analysis of the image histogram. Being universal, the scheme does not require knowledge of the detection algorithms available to the forensic analyst, and can be used to conceal traces left in the histogram of the image by any processing tool. Instead of adapting the histogram of the image to fit some statistical model, the proposed scheme makes it practically identical to the histogram of an untouched image, by solving an optimization problem. In doing this, the perceptual similarity between the processed and counter-attacked image is preserved to a large extent. The validity of the scheme in countering both contrast-enhancement and splicing- detection is assessed through experimental validation.

Digital Image Forensics Using Multi-Resolution Histograms

2012 ◽  
pp. 163-175
Author(s):  
Jin Liu ◽  
Hefei Ling ◽  
Fuhao Zou ◽  
WeiQi Yan ◽  
Zhengding Lu
Keyword(s):  
Digital Image ◽  
Image Forensics ◽  
Experimental Results ◽  
Digital Image Forensics ◽  
Clone Detection ◽  
Splicing Detection

In this paper, the authors investigate the prospect of using multi-resolution histograms (MRH) in conjunction with digital image forensics, particularly in the detection of two kinds of copy-move manipulations, i.e., cloning and splicing. To the best of the authors’ knowledge, this is the first work that uses the same feature in both cloning and splicing forensics. The experimental results show the simplicity and efficiency of using MRH for the purpose of clone detection and splicing detection.

Linear Histogram Adjustment for Image Enhancement

2020 ◽  
Vol 2020 (1) ◽  
pp. 65-68
Author(s):  
Jake McVey
Keyword(s):  
Contrast Enhancement ◽  
Image Enhancement ◽  
Input Image ◽  
Surprising Result ◽  
Image Histogram ◽  
Non Linear ◽  
Linear Transform

Tone curves are one of the simplest techniques for image enhancement. Specified as a function, a tone curve is a transformation that maps pixel levels of an input image to new output levels. Tone curves are the basis of many contrast enhancement algorithms, including Contrast Limited Histogram Equalisation (CLHE), which derives a tone curve from a modification of the image histogram. While these methods can provide good enhancement, they are generally non-linear. In this paper we show the surprising result that a tone curve generated by the non-linear CLHE method (and HE) can be calculated by applying a linear transform to the histogram of the input image. Experiments validate our method.

scholarly journals Panoramic Dental Radiography Image Enhancement Using Multiscale Mathematical Morphology

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3110
Author(s):  
Julio César Mello Román ◽  
Vicente R. Fretes ◽  
Carlos G. Adorno ◽  
Ricardo Gariba Silva ◽  
José Luis Vázquez Noguera ◽  
...  
Keyword(s):  
Contrast Enhancement ◽  
Mathematical Morphology ◽  
Panoramic Radiography ◽  
Histogram Equalization ◽  
Dental Radiography ◽  
Panoramic Radiographs ◽  
Image Histogram ◽  
Multi Scale ◽  
Dental Radiographs

Panoramic dental radiography is one of the most used images of the different dental specialties. This radiography provides information about the anatomical structures of the teeth. The correct evaluation of these radiographs is associated with a good quality of the image obtained. In this study, 598 patients were consecutively selected to undergo dental panoramic radiography at the Department of Radiology of the Faculty of Dentistry, Universidad Nacional de Asunción. Contrast enhancement techniques are used to enhance the visual quality of panoramic dental radiographs. Specifically, this article presents a new algorithm for contrast, detail and edge enhancement of panoramic dental radiographs. The proposed algorithm is called Multi-Scale Top-Hat transform powered by Geodesic Reconstruction for panoramic dental radiography enhancement (MSTHGR). This algorithm is based on multi-scale mathematical morphology techniques. The proposal extracts multiple features of brightness and darkness, through the reconstruction of the marker (obtained by the Top-Hat transformation by reconstruction) starting from the mask (obtained by the classic Top-Hat transformation). The maximum characteristics of brightness and darkness are added to the dental panoramic radiography. In this way, the contrast, details and edges of the panoramic radiographs of teeth are improved. For the tests, MSTHGR was compared with the following algorithms: Geodesic Reconstruction Multiscale Morphology Contrast Enhancement (GRMMCE), Histogram Equalization (HE), Brightness Preserving Bi-Histogram Equalization (BBHE), Dual Sub-Image Histogram Equalization (DSIHE), Minimum Mean Brightness Error Bi-Histogram Equalization (MMBEBHE), Quadri-Histogram Equalization with Limited Contrast (QHELC), Contrast-Limited Adaptive Histogram Equalization (CLAHE) and Gamma Correction (GC). Experimentally, the numerical results show that the MSTHGR obtained the best results with respect to the Contrast Improvement Ratio (CIR), Entropy (E) and Spatial Frequency (SF) metrics. This indicates that the algorithm performs better local enhancements on panoramic radiographs, improving their details and edges.

Entropy based Range Optimized Brightness Preserved Histogram-Equalization for Image Contrast Enhancement

2016 ◽  
Vol 6 (1) ◽  
pp. 59-72 ◽  
Author(s):  
Krishna Gopal Dhal ◽  
Sankhadip Sen ◽  
Kaustav Sarkar ◽  
Sanjoy Das
Keyword(s):  
Contrast Enhancement ◽  
Signal To Noise Ratio ◽  
Histogram Equalization ◽  
Image Contrast ◽  
Experimental Results ◽  
Signal To Noise ◽  
Image Histogram ◽  
Image Contrast Enhancement ◽  
Noise Ratio

In this study the over-enhancement problem of traditional Histogram-Equalization (HE) has been removed to some extent by a variant of HE called Range Optimized Entropy based Bi-Histogram Equalization (ROEBHE). In ROEBHE image histogram has been thresholded into two sub-histograms i.e. histograms corresponding to background and foreground. The threshold is calculated by maximizing the sum of the entropy of these two sub-histograms. The range for equalization has been optimized by maximizing the Peak-Signal to Noise ratio (PSNR). The experimental results prove that ROEBHE has prevailed over existing methods and PSNR is a better range optimizer than Absolute Mean Brightness Error (AMBE).

Sign in / Sign up

Export Citation Format

Share Document