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.

Tampering Localization in Double Compressed Images by Investigating Noise Quantization

2020 ◽  
pp. 336-353
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.

A CRYPTO SIGNATURE SCHEME FOR IMAGE AUTHENTICATION OVER WIRELESS CHANNEL

2005 ◽  
Vol 05 (01) ◽  
pp. 135-148 ◽  
Author(s):  
QIBIN SUN ◽  
SHUIMING YE ◽  
CHING-YUNG LIN ◽  
SHIH-FU CHANG
Keyword(s):  
Packet Loss ◽  
Digital Signature ◽  
Digital Images ◽  
Image Authentication ◽  
Wireless Channel ◽  
Experimental Results ◽  
Signature Scheme ◽  
Original Image ◽  
Degraded Images ◽  
Block Shuffling

With the ambient use of digital images and the increasing concern on their integrity and originality, consumers are facing an emergent need of authenticating degraded images despite lossy compression and packet loss. In this paper, we propose a scheme to meet this need by incorporating watermarking solution into traditional cryptographic signature scheme to make the digital signatures robust to these image degradations. Due to the unpredictable degradations, the pre-processing and block shuffling techniques are applied onto the image at the signing end to stabilize the feature extracted at the verification end. The proposed approach is compatible with traditional cryptographic signature scheme except that the original image needs to be watermarked in order to guarantee the robustness of its derived digital signature. We demonstrate the effectiveness of this proposed scheme through practical experimental results.

scholarly journals Using the Relevance Vector Machine Model Combined with Local Phase Quantization to Predict Protein-Protein Interactions from Protein Sequences

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Ji-Yong An ◽  
Fan-Rong Meng ◽  
Zhu-Hong You ◽  
Yu-Hong Fang ◽  
Yu-Jun Zhao ◽  
...  
Keyword(s):  
Protein Sequences ◽  
Relevance Vector Machine ◽  
Experimental Results ◽  
Computational Method ◽  
Support Vector ◽  
Svm Classifier ◽  
Local Phase ◽  
Local Phase Quantization ◽  
Phase Quantization ◽  
Better Than

We propose a novel computational method known as RVM-LPQ that combines the Relevance Vector Machine (RVM) model and Local Phase Quantization (LPQ) to predict PPIs from protein sequences. The main improvements are the results of representing protein sequences using the LPQ feature representation on a Position Specific Scoring Matrix (PSSM), reducing the influence of noise using a Principal Component Analysis (PCA), and using a Relevance Vector Machine (RVM) based classifier. We perform 5-fold cross-validation experiments onYeastandHumandatasets, and we achieve very high accuracies of 92.65% and 97.62%, respectively, which is significantly better than previous works. To further evaluate the proposed method, we compare it with the state-of-the-art support vector machine (SVM) classifier on theYeastdataset. The experimental results demonstrate that our RVM-LPQ method is obviously better than the SVM-based method. The promising experimental results show the efficiency and simplicity of the proposed method, which can be an automatic decision support tool for future proteomics research.

Tradeoffs Between Forensics and Anti-Forensics of Digital Images

2018 ◽  
pp. 2124-2138
Author(s):  
Priya Makarand Shelke ◽  
Rajesh Shardanand Prasad
Keyword(s):  
Social Networking ◽  
Digital Images ◽  
Image Authentication ◽  
Visual Media ◽  
Smart Phones ◽  
The Internet ◽  
Image Processing Techniques ◽  
Image Tampering ◽  
Processing Techniques ◽  
And Storage

Over past few years, we are the spectators of the evolution in the field of information technology, telecommunication and networking. Due to the advancement of smart phones, easy and inexpensive access to the internet and popularity of social networking, capture and use of digital images has increased drastically. Image processing techniques are getting developed at rapidly and at the same time easy to use image tampering soft-wares are also getting readily available. If tampered images are misused, big troubles having deep moral, ethical and lawful allegations may arise. Due to high potential of visual media and the ease in their capture, distribution and storage, we rarely find a field where digital visual data is not used. The value of image as evidence of event must be carefully assessed and it is a call for from different fields of applications. Therefore, in this age of fantasy, image authentication has become an issue of utmost importance.

Improving Self-interacting Proteins Prediction Accuracy Using Protein Evolutionary Information and Weighed-Extreme Learning Machine

2019 ◽  
Vol 14 (2) ◽  
pp. 115-122 ◽  
Author(s):  
Ji-Yong An ◽  
Yong Zhou ◽  
Lei Zhang ◽  
Qiang Niu ◽  
Da-Fu Wang
Keyword(s):  
Extreme Learning Machine ◽  
Functional Organization ◽  
Web Server ◽  
Feature Representation ◽  
Experimental Results ◽  
Evolutionary Information ◽  
Svm Classifier ◽  
Interacting Proteins ◽  
Learning Machine ◽  
Human Dataset

Background: Self Interacting Proteins (SIPs) play an essential role in various aspects of the structural and functional organization of the cell. Objective: In the study, we presented a novelty sequence-based computational approach for predicting Self-interacting proteins using Weighed-Extreme Learning Machine (WELM) model combined with an Autocorrelation (AC) descriptor protein feature representation. Method: The major advantage of the proposed method mainly lies in adopting an effective feature extraction method to represent candidate self-interacting proteins by using the evolutionary information embedded in PSI-BLAST-constructed Position Specific Scoring Matrix (PSSM); and then employing a reliable and effective WELM classifier to perform classify. </P><P> Result: In order to evaluate the performance, the proposed approach is applied to yeast and human SIP datasets. The experimental results show that our method obtained 93.43% and 98.15% prediction accuracies on yeast and human dataset, respectively. Extensive experiments are carried out to compare our approach with the SVM classifier and existing sequence-based method on yeast and human dataset. Experimental results show that the performance of our method is better than several other state-of-theart methods. Conclusion: It is demonstrated that the proposed method is suitable for SIPs detection and can execute incredibly well for identifying Sips. In order to facilitate extensive studies for future proteomics research, we developed a freely available web server called WELM-AC-SIPs in Hypertext Preprocessor (PHP) for predicting SIPs. The web server including source code and the datasets are available at http://219.219.62.123:8888/WELMAC/.

An Algorithmic Approach Based on CMS Edge Detection Technique for the Processing of Digital Images

2020 ◽  
pp. 252-272
Author(s):  
Kalyan Kumar Jena ◽  
Sasmita Mishra ◽  
Sarojananda Mishra
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Digital Image Processing ◽  
Digital Images ◽  
Color Images ◽  
Experimental Results ◽  
Detection Technique ◽  
Algorithmic Approach ◽  
Current Scenario ◽  
Edge Detection Method

Research in the field of digital image processing (DIP) has increased in the current scenario. Edge detection of digital images is considered as an important area of research in DIP. Detecting edges in different digital images accurately is a challenging work in DIP. Different methods have been introduced by different researchers to detect the edges of images. However, no method works well under all conditions. In this chapter, an edge detection method is proposed to detect the edges of gray scale and color images. This method focuses on the combination of Canny, mathematical morphological, and Sobel (CMS) edge detection operators. The output of the proposed method is produced using matrix laboratory (MATLAB) R2015b and compared with Sobel, Prewitt, Roberts, Laplacian of Gaussian (LoG), Canny, and mathematical morphological edge detection operators. The experimental results show that the proposed method works better as compared to other existing methods in detecting the edges of images.

A novel method for detecting light source for digital images forensic

2011 ◽  
Vol 19 (2) ◽  
Author(s):  
A. Roy ◽  
S. Mitra ◽  
R. Agrawal
Keyword(s):  
Forensic Science ◽  
Light Source ◽  
Digital Image ◽  
Digital Images ◽  
Experimental Results ◽  
New Method ◽  
Light Sources ◽  
Image Forensic ◽  
Morality Politics ◽  
Novel Method

AbstractManipulation in image has been in practice since centuries. These manipulated images are intended to alter facts — facts of ethics, morality, politics, sex, celebrity or chaos. Image forensic science is used to detect these manipulations in a digital image. There are several standard ways to analyze an image for manipulation. Each one has some limitation. Also very rarely any method tried to capitalize on the way image was taken by the camera. We propose a new method that is based on light and its shade as light and shade are the fundamental input resources that may carry all the information of the image. The proposed method measures the direction of light source and uses the light based technique for identification of any intentional partial manipulation in the said digital image. The method is tested for known manipulated images to correctly identify the light sources. The light source of an image is measured in terms of angle. The experimental results show the robustness of the methodology.

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