Peak signal to noise ratio (PSNR) and structural index similarity (SSIM) are two measuring tools that are widely used in image quality assessment. Especially in the steganography image, these two measuring instruments are used to measure the quality of imperceptibility. PSNR is used earlier than SSIM, is easy, has been widely used in various digital image measurements, and has been considered tested and valid. SSIM is a newer measurement tool that is designed based on three factors i.e. luminance, contrast, and structure to better suit the workings of the human visual system. Some research has discussed the correlation and comparison of these two measuring tools, but no research explicitly discusses and suggests which measurement tool is more suitable for steganography. This study aims to review, prove, and analyze the results of PSNR and SSIM measurements on three spatial domain image steganography methods, i.e. LSB, PVD, and CRT. Color images were chosen as container images because human vision is more sensitive to color changes than grayscale changes. Based on the test results found several opposing findings, where LSB has the most superior value based on PSNR and PVD get the most superior value based on SSIM. Additionally, the changes based on the histogram are more noticeable in LSB and CRT than in PVD. Other analyzes such as RS attack also show results that are more in line with SSIM measurements when compared to PSNR. Based on the results of testing and analysis, this research concludes that SSIM is a better measure of imperceptibility in all aspects and it is preferable that in the next steganographic research at least use SSIM.

PSNR vs SSIM: imperceptibility quality assessment for image steganography

We introduce a novel deep neural architecture for image copy-move forgery detection (CMFD), code-named BusterNet. Unlike previous efforts, BusterNet is a pure, end-to-end trainable, deep neural network solution. It features a two-branch architecture followed by a fusion module. The two branches localize potential manipulation regions via visual artifacts and copy-move regions via visual similarities, respectively. To the best of our knowledge, this is the first CMFD algorithm with discernibility to localize source/target regions. We also propose simple schemes for synthesizing large-scale CMFD samples using out-of-domain datasets, and stage-wise strategies for effective BusterNet training. Our extensive studies demonstrate that BusterNet outperforms state-of-the-art copy-move detection algorithms by a large margin on the two publicly available datasets, CASIA and CoMoFoD, and that it is robust against various known attacks.

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BusterNet: Detecting Copy-Move Image Forgery with Source/Target Localization

Since powerful editing software is easily accessible, manipulation on images is expedient and easy without leaving any noticeable evidences. Hence, it turns out to be a challenging chore to authenticate the genuineness of images as it is impossible for human's naked eye to distinguish between the tampered image and actual image. Among the most common methods extensively used to copy and paste regions within the same image in tampering image is the copy-move method. Discrete Cosine Transform (DCT) has the ability to detect tampered regions accurately. Nevertheless, in terms of precision (FP) and recall (FN), the block size of overlapping block influenced the performance. In this paper, the researchers implemented the copy-move image forgery detection using DCT coefficient. Firstly, by using the standard image conversion technique, RGB image is transformed into grayscale image. Consequently, grayscale image is segregated into overlying blocks of m × m pixels, m = 4.8. 2D DCT coefficients are calculated and reposition into a feature vector using zig-zag scanning in every block. Eventually, lexicographic sort is used to sort the feature vectors. Finally, the duplicated block is located by the Euclidean Distance. In order to gauge the performance of the copy-move detection techniques with various block sizes with respect to accuracy and storage, threshold D_similar = 0.1 and distance threshold (N)_d = 100 are used to implement the 10 input images in order. Consequently, 4 × 4 overlying block size had high false positive thus decreased the accuracy of forged detection in terms of accuracy. However, 8 × 8 overlying block accomplished more accurately for forged detection in terms of precision and recall as compared to 4 × 4 overlying block. In a nutshell, the result of the accuracy performance of different overlying block size are influenced by the diverse size of forged area, distance between two forged areas and threshold value used for the research.

Detection of copy-move image forgery based on discrete cosine transform

A robust technique for copy-move forgery detection and localization in digital images via stationary wavelet and discrete cosine transform

Due to the recent technology development, the multimedia complexity is noticeably increased and new research areas are opened relying on similar multimedia content retrieval. Content-based image retrieval (CBIR) systems are used for the retrieval of images related to the Query Image (QI) from huge databases. The CBIR systems available today have confined efficiency as they extract only limited feature sets. This paper demonstrates the extraction of vast robust and important features from the images database and the storage of these features in the repository in the form of feature vectors. The feature repository contains color signature, the shape features and texture features. Here, features are extracted from specific QI. Accordingly, an innovative similarity evaluation with a metaheuristic algorithm (genetic algorithm with simulating annealing) has been attained between the QI features and those belonging to the database images. For an image entered as QI from a database, the distance metrics are used to search the related images, which is the main idea of CBIR. The proposed CBIR techniques are described and constructed based on RGB color with neutrosophic clustering algorithm and Canny edge method to extract shape features, YCbCr color with discrete wavelet transform and Canny edge histogram to extract color features, and gray-level co-occurrence matrix to extract texture features. The combination of these methods increases the image retrieval framework performance for content-based retrieval. Furthermore, the results’ precision–recall value is calculated to evaluate the system’s efficiency. The CBIR system proposed demonstrates better precision and recall values compared to other state-of-the-art CBIR systems.

Content-Based Image Retrieval Using Color, Shape and Texture Descriptors and Features

Due to the powerful image editing tools images are open to several manipulations; therefore, their authenticity is becoming questionable especially when images have influential power, for example, in a court of law, news reports, and insurance claims. Image forensic techniques determine the integrity of images by applying various high-tech mechanisms developed in the literature. In this paper, the images are analyzed for a particular type of forgery where a region of an image is copied and pasted onto the same image to create a duplication or to conceal some existing objects. To detect the copy-move forgery attack, images are first divided into overlapping square blocks and DCT components are adopted as the block representations. Due to the high dimensional nature of the feature space, Gaussian RBF kernel PCA is applied to achieve the reduced dimensional feature vector representation that also improved the efficiency during the feature matching. Extensive experiments are performed to evaluate the proposed method in comparison to state of the art. The experimental results reveal that the proposed technique precisely determines the copy-move forgery even when the images are contaminated with blurring, noise, and compression and can effectively detect multiple copy-move forgeries. Hence, the proposed technique provides a computationally efficient and reliable way of copy-move forgery detection that increases the credibility of images in evidence centered applications.

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Copy-Move Forgery Detection Technique for Forensic Analysis in Digital Images

In today's modern life, digital images have significant importance because they have become a leading source of information dissemination. However, the availability of image editing tools made it easier to forge the contents of a digital image; making the authenticity untrustful. Different techniques can be used to forge the digital images. Copy move forgery is the most popular and common approach where a specific part of an image is copied and pasted elsewhere in the same image to conceal unwanted part or object. In this study, we attempted to survey several passive block based copy move forgery detection techniques. A passive technique attempts to identify forgery in digital images without any prior information. A comparison between various techniques is also included.

A survey on block based copy move image forgery detection techniques

The rapid development of data communication in modern era demands secure exchange of information. Steganography is an established method for hiding data from an unauthorised access. Steganographic techniques hide secret data in different file formats such as: image, text, audio, and video. Invisibility, payload capacity, and security in terms of PSNR and robustness are the key challenges to steganography. In this paper, a novel image stegnography technique based on most significant bits (MSB) of image pixels is proposed. Bit No. 5 is used to store the secret bits based on the difference of bit No. 5 and 6 of cover image. If the difference of bit No. 5 and 6 is different from secret data bit then the value of bit No. 5 is changed. The results state that the proposed technique ensures significant improvements in signal to noise ratio. Usually, the hackers focus on LSB bits for secret data extraction but the proposed technique utilizes the MSB bits that make it more secure from unauthorized access. Furthermore, the presented technique is not only secure, but computationally efficient as well.

An improved image steganography technique based on MSB using bit differencing

The internet users share a massive amount of digital images daily. The accessibility of powerful image manipulation tools has made the integrity of image contents questionable. The most popular image tampering is to duplicate a region elsewhere in the same image to replicate or conceal some other region. The duplicated regions have identical color and texture attributes that make this artifact invisible to the human eye. Therefore, efficient techniques are required to verify the credibility of image contents by detecting the regions duplicated in the digital images. This paper proposes an efficient technique for exposing region duplication forgery in digital images. The proposed technique divides the approximation (LL) sub-band of shift invariant stationary wavelet transform into overlapping blocks of w × w (i.e. w = 4, 8) sizes. The distinctive features extracted from the overlapping blocks are utilized to expose the region duplication forgeries in digital images. The experimental results of the proposed technique are compared with state-of-the-art techniques that reveal the prominence, and effectiveness of the proposed technique in terms of precision, recall and F1 score for different block sizes. Therefore, the proposed technique can reliably be applied to identify the counterfeited regions and the benefits of the proposed technique can be achieved in different fields for example crime investigation, news reporting, and judiciary.

Mohsin Shah

Papers

A robust technique for copy-move forgery detection and localization in digital images via stationary wavelet and discrete cosine transform

Copy-Move Forgery Detection Technique for Forensic Analysis in Digital Images

A survey on block based copy move image forgery detection techniques

An improved image steganography technique based on MSB using bit differencing

An efficient forensic technique for exposing region duplication forgery in digital images