JPEG Research Articles

Exposing JPEG compression footprints by using second-order statistical analysis

Exposing JPEG compression footprints by using second-order statistical analysis

Employing Algebraic Eigenvalue Decomposition in Zero Watermarking Technology

The new watermarking technology is highly sophisticated, utilizing advanced algorithms to ensure robust protection and seamless integration with digital content while maintaining the highest levels of security against potential attacks. The primary goal of this paper is to enhance the zero watermarking technology by leveraging the eigenvalue decomposition (EVD) method as an algebraic transformation, without relying on other common transforms. The image matrix is divided into 4x4 blocks, and EVD is applied to each block to extract feature matrix bits, forming the final zero-secret. The robustness of the proposed algorithm is evaluated, showing robust resilience to conventional attacks like noise, filtering, JPEG compression, and cropping, as indicated by the normalized correlation (NC) values.

JPEG Image Encryption With DC Rotation and Undivided RSV-Based AC Group Permutation

JPEG Image Encryption With DC Rotation and Undivided RSV-Based AC Group Permutation

JSN: Design and Analysis of JPEG Steganography Network

Image steganography involves hiding a secret message within an image for covert communication, allowing only the intended recipient to extract the hidden message from the “stego” image. The secret message can also be an image itself to enable the transmission of more information, resulting in applications where one image is concealed within another. While existing techniques can embed a secret image of similar size into a cover image with minimal distortion, they often overlook the effects of lossy compression during transmission, such as when saving images in the commonly used JPEG format. This oversight can hinder the extraction of the hidden image. To address the challenges posed by JPEG compression in image steganography, we propose a JPEG Steganography Network (JSN) that leverages a reversible deep neural network as its backbone, integrated with the JPEG encoding process. We utilize 8×8 Discrete Cosine Transform (DCT) and consider the quantization step size specified by JPEG to create a JPEG-compliant stego image. We also discuss various design considerations and conduct extensive testing on JSN to validate its performance and practicality in real-world applications.

Can we trust the accuracy of the automatic calibration of the EOS system to measure lower limb length inequality after total hip arthroplasty? Comparison of EOS versus manual measurement on 110 calibrated radiographs

IntroductionTraditionally, to determine a length on a limb radiograph after total hip arthroplasty (THA), calibration is performed manually with the diameter of a ball or the femoral head. More recently, the development of EOS with automatic calibration has called into question the usefulness of manual calibration to highlight lower limb length inequality (LLLI). However, the validation of EOS with automatic calibration without landmarks to measure length inequalities on large images has not been verified against manual measurements on calibrated radiographs (conventional method), which motivated the present work. HypothesisEOS is more accurate and reproducible from one reader to another in highlighting LLLI after THA than the classic method. Patients and methodsOne hundred and ten patients included underwent primary THA surgery in 2 centers, with postoperative EOS performed. This EOS was extracted in 2 formats: a DICOM file with automatic calibration and an uncalibrated JPEG file (220 radiographic files in total).Two readers, without knowledge of the clinical data, each analyzed all of these images using 2 methods: by measuring the distance between the center of the femoral head and the center of the mortise on the operated side and the non-operated side on the DICOM file, therefore via the software integrated into the EOS image (method 1), or by measuring these same distances on the JPEG file by calibrating the measurement to the diameter of the prosthetic head (method 2). The reproducibility of the measurements from one reader to another (inter-observer reproducibility) and the agreement of the measurements between the 2 methods (inter-method agreement) were evaluated using the intraclass correlation coefficient (ICC) and the Bland- Altman graphic method. ResultsInter-method agreement was satisfactory regardless of the reader but with a significantly higher agreement for reader 1 (inter-method agreement for reader 1: CL1 = 0.95 and for reader 2: CL2 = 0.90 (p = 0.008)) and this result was also confirmed by the Bland-Altman plot with no bias tendency for each reader and less than 5% of measurements that were outside the agreement band. Inter-observer reproducibility for method 1 was better than that for method 2 to highlight an LLLI after THA according to the CCI. Inter-observer reproducibility for method 1 (RM1) was 0.96 and 0.92 for method 2 (RM2) (p = 0.009). This result was confirmed by the Bland-Altman plot with a mean difference of less than 1 mm for each of the methods (−0.2 (standard deviation = 2.23) and 0.81 (3.03) for method 1 and method 2 respectively). DiscussionOur hypothesis has therefore been partially verified. The use of the calibrated software integrated into the EOS system and manual calibration are two valid methods for searching for an LLLI. The measurements remain more reproducible from one reader to another with the calibrated software integrated into the EOS system. Level of evidenceIII; non-randomized prospective comparative study.

A case study on entropy-aware block-based linear transforms for lossless image compression

Data compression algorithms tend to reduce information entropy, which is crucial, especially in the case of images, as they are data intensive. In this regard, lossless image data compression is especially challenging. Many popular lossless compression methods incorporate predictions and various types of pixel transformations, in order to reduce the information entropy of an image. In this paper, a block optimisation programming framework Φ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Phi$$\\end{document} is introduced to support various experiments on raster images, divided into blocks of pixels. Eleven methods were implemented within Φ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Phi$$\\end{document}, including prediction methods, string transformation methods, and inverse distance weighting, as a representative of interpolation methods. Thirty-two different greyscale raster images with varying resolutions and contents were used in the experiments. It was shown that Φ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Phi$$\\end{document} reduces information entropy better than the popular JPEG LS and CALIC predictors. The additional information associated with each block in Φ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Phi$$\\end{document} is then evaluated. It was confirmed that, despite this additional cost, the estimated size in bytes is smaller in comparison to the sizes achieved by the JPEG LS and CALIC predictors.

Advanced Reversible Data Hiding Techniques in JPEG Images: Methods, Applications, and Future Perspectives

This paper provides an extensive review of recent advancements in reversible information hiding methods tailored for JPEG images, a predominant compression standard crucial for data security and digital copyright protection. It starts by outlining fundamental concepts related to JPEG image structure and reversible data hiding techniques. The analysis then delves into three primary methods: modifications to JPEG quantization tables, adjustments in Huffman tables, and corrections of Discrete Cosine Transform (DCT) coefficients, detailing how each technique contributes to effective and secure data embedding. Furthermore, the exploration of potential applications in critical areas highlights the versatile utility of these techniques in safeguarding digital content. Concluding with a discussion on future research directions, this paper emphasizes the significant potential for growth in this field, particularly in enhancing security measures and improving efficiency. Such advancements promise to extend the applicability of reversible data hiding, ensuring robust copyright protection and information security in the digital age.

Justification of Image Format Structure for Solving Pattern Recognition Problems in Environmental Monitoring

Introduction. Environmental monitoring of marine areas and timely detection of the results of man-made disasters on the water surface, including identification of oil contaminated areas, represents an urgent task. To facilitate its solution, marine areas are controlled using space and airborne means. However, the volume of data subject to control is constantly growing. Therefore, the problem can be solved by selecting only those photo and video materials with detected traces of oil spills and other man-made disasters.Aim. To develop an approach to automatic selection of images obtained from visual control systems, providing only relevant images.Materials and methods. The theoretical part of the study was carried out using a classification method based on pattern recognition theory. A test image with an oil spill on the surface of the Black Sea was compared with the same image presented in BMP format with different color depth encodings, as well as with the same image presented in JPEG format. Raster images were processed using a specialized software application. Simulation was carried out in the MathCAD environment.Results. The developed approach was tested experimentally by processing 200 images. The conducted visual analysis confirmed that the image, where the given boundary allows the area of oil spill to be clearly distinguishes, is the closest to the test image.Conclusion. According to the results of the experiment with different formats of raster image files, the conclusion is made about the feasibility of using images obtained by visual control systems presented in JPEG format as initial data. Further research directions are outlined.

Ocular Duction Measurement Using Three Convolutional Neural Network Models: A Comparative Study.

This study primarily aimed to compare the accuracy of three convolutional neural network (CNN) models in measuring the four positions of ocular duction. Further, it secondarily aimed to compare the accuracy of each CNN model in the training dataset versus the ophthalmologist measurements. This study included 526 subjects aged over 18 who visited the ophthalmology outpatient department. Ocular images were captured using mobile phones in various gaze positions and stored anonymously as JPEG files. Ocular duction was measured by assessing corneal light reflex deviation from the central cornea. Ductions were classified into 30, 60, and 90 prism diopters (PD) and full ductions from the primary position. Three CNN models, MobileNet, ResNet, and EfficientNet, were used to classify ocular duction. Their predictive ability was evaluated using the area under the receiver operating characteristic (AUROC) curve. The dataset was divided into the training (2,001 images), evaluation (213 images), and testing (190 images) groups, which were reconstructed using the routine follow-up data of volunteers at the Ophthalmology Department of Phramongkutklao Hospital between February 2023 and June 2023. To evaluate the data, the MobileNet_V3_Large, ResNet101, and EfficientNet_B5 models were utilized to measure duction angles with the receiver operating characteristic (ROC) curves. The training times for MobileNet, ResNet, and EfficientNet were 5.54, 9.56, and 26.39 minutes, respectively. In the testing phase, MobileNet, ResNet, and EfficientNet were used to measure each duction position: 30 PD with corresponding ROC curve values of 0.77, 0.5, and 0.58; 60 PD with ROC curve values of 0.71, 0.83, and 0.81; 90 PD with ROC curve values of 0.7, 0.73, and 0.81; and full duction with ROC curve values of 0.91, 0.93, and 0.94, respectively. Analysis of variance revealed no significant difference in the mean AUROC curves among the models, yielding a p-value of 0.936.MobileNet has the narrowest confidence intervals for average prediction accuracy across three CNN models. The three CNN models did not significantly differ in terms of efficacy in detecting various duction positions. However, MobileNet stands out, with a narrower confidence interval and shorter training time, which indicates its potential application.

Reversible Data Hiding in Shared JPEG Images

Reversible data hiding (RDH) in encrypted images has emerged as an effective technique for securely storing and managing confidential images in the cloud. However, most RDH methods in shared images (RDHSI) are designed for uncompressed images and cannot be applied for JPEG images. To address this issue, we propose a novel RDH in shared JPEG images. Our method consists of JPEG image sharing and data hiding in JPEG shares, which are both conducted on JPEG bit-stream. Specifically, the DC appended bits (DCA) and AC appended bits (ACA) derived from the original JPEG bit-stream are shared by ( \(k\) , \(n\) ) threshold Chinese remainder theorem-based secret sharing (CRTSS) with two different constraints, one for DC sharing and another for AC sharing. The constraint of DC sharing ensures that the DC coefficient shares do not overflow. The constraint of AC sharing ensures the sizes of ACA shares are less than the sizes of the original ACA so that the embedding room can be vacated from each shared JPEG bit-stream. Each data-hider can embed the secret data into the personal JPEG share. The original JPEG image can be recovered losslessly from any \(k\) JPEG shares. The proposed sharing and data hiding are both well compatible with the JPEG standard. Experimental results demonstrate that the proposed method not only well preserves the file size whether the JPEG shares or marked JPEG shares but also achieves outstanding security performance and a high embedding capacity.

A Coverless Image Steganography Based on Huffman Encoding and Robust Image Wavelet Hashing

Data hiding approaches are crucial in insecure communication to protect sensi- tive information from illegal access. The robustness of existing coverless image steganogra- phy methods that utilize mapping rules is derived from their ability to extract feature patterns within the spatial domain. In order to protect sensitive data more securely and to increase its durability against attacks, a new coverless steganography is introduced in this study. The me- thod used is based on the frequency domain. A coverless image steganography based on ro- bust image wavelet hashing and Huffman encoding. Initially, the confidential data is encoded through lossless compression using Huffman Encoding, which reduces the payload and en- hances the capacity for embedding. Second, segments of an 8-bit size are created from the compressed secret data. The efficient DWT hashing technique has been employed to generate a hash sequence for an image. An inverted index structure is established, allowing for the se- lection of the image whose hash corresponds to the secret data segment. Subsequently, all chosen images along with supplementary information are transmitted to the recipient. Testing has shown that the proposed method is robust against various image processing attacks, in- cluding scaling, low pass filtering, JPEG compression, rotation, noise introduction, and me- dian/mean filtering. Research findings and analysis indicate that this method enhances the latest coverless steganography algorithms in terms of capacity, resilience, and security.

Text Data Security Using LCG and CBC with Steganography Technique on Digital Image

This research proposes a text data security method using a combination of Linear Congruential Generator (LCG), Advanced Encryption Standard (AES) Cipher Block Chaining (CBC) mode, and Least Significant Bit (LSB) steganography technique on digital images. The message scrambling process using LCG produces ASCII characters as noise that is inserted in the original message. After that, the message is encrypted using AES-256 CBC to provide additional security. The encryption result is then hidden in the digital image through LSB steganography technique. Tests were conducted on images with JPEG and BMP formats to measure the visual quality after the data insertion process, as measured by PSNR (Peak Signal-to-Noise Ratio). The test results show a PSNR value of 56.60 dB for JPEG images and 70.84 dB for BMP images. In addition, the insertion process in JPEG images degrades the image quality, mainly due to lossy compression, compared to the lossless BMP format. This study concludes that the proposed combination of methods is effective in hiding messages in images, but is susceptible to compression on lossy formats such as JPEG. The use of lossless image formats such as BMP or PNG is recommended to maintain data integrity.

CONCEALING TEXT AND EXECUTABLE FILES IN JPG FORMAT FILES WITH A STEGANOGRAPHIC COMMAND-LINE TOOL IN THE OPERATING SYSTEM KALI LINUX

In this scientific paper concealing text and executable files in JPG format files with a steganographic command-line tool in the Kali Linux operating system is presented. Among the myriad of steganographic tools, Steghide has gained prominence for its robust features and ease of use. Primarily focused on embedding and extracting secret messages within image files, the software tool Steghide enables users to encode text data within JPEG and BMP image formats while maintaining data integrity and minimizing visual alterations. This paper provides an in-depth examination of Steghide’s functionality, workflow, and security considerations in the context of digital steganography. It is also discussed the algorithms underpinning Steghide’s operations, its strengths, limitations and common use cases.

Increase of Reliability of Anomalies Detection on Images at Formation of Their Feature Vectors in Wavelet Bases

The method of detection of life rafts and lifeboats in the water area of seas and oceans after shipwrecks based on the recognition of anomalies in the processed images, which increases the probability of recognition of monitoring objects, is proposed. The approach to solving such a problem is substantiated. The formulation of the problem of object recognition from the perspective of binary classification in the detection of anomalies is presented. The analytical expression for the decision-making algorithm is obtained. The possibility of formalization of image matrices in the form of histograms of color (brightness) intensity distributions is considered. The contrast of the feature space on their basis is estimated. It is suggested that the contrast of feature spaces be increased due to the secondary processing of histograms of distributions in the basis of multiple-scale wavelet decomposition. The possibility of realization of wavelet transformations on the basis of Haar functions and Gauss wavelets of the 1st and 2nd orders is considered. The mechanism of formation of secondary feature vectors from three-dimensional wavelet transforms by averaging their coefficients along the time shift axis is substantiated. It is shown that at the same dimensionality of histograms of brightness distribution with newly formed feature vectors, the latter provide higher contrast of feature spaces. It is recommended to use a Gaussian wavelet of the 2nd order for the formalization of images in jpeg format, which provides, other things being equal, a greater magnitude of differences for images containing anomalies. An approach to probabilistic evaluation of the algorithm for automatic image recognition is developed. The analytical expression is obtained and its constituent elements are justified. Graphical dependences of the probability of correct detection (recognition) of anomalies, depending on the size in relation to the total area of the frame and the dispersion of the underlying background are given. The results of the experiment on image recognition with a lifeboat in the ocean water area are presented. The directions of further research are defined.

Learning Compressed Artifact for JPEG Manipulation Localization Using Wide-Receptive-Field Network

JPEG image manipulation localization aims to accurately classify and locate tampered regions in JPEG images. Existing image manipulation localization schemes usually consider diverse data streams of spatial domain, e.g. noise inconsistency and local content inconsistency. They, however, easily ignore an objective scenario: data stream features of spatial domain are hard to directly apply to compressed image format, e.g., JPEG, because tampered JPEG images may contain severe re-compression inconsistency and re-compression artifacts, when they are re-compressed to JPEG format. As a result, the traditional localization schemes relying on general data streams of spatial domain may result in a large number of false detection of tampered region in JPEG images. To address the above problem, we a new JPEG image manipulation localization scheme, in which a wide-receptive-field attention network is designed to effectively learn JPEG compressed artifacts. We firstly introduce the wide-receptive-field attention mechanism to re-construct U-Net network, which can effectively capture contextual information of JPEG images and analyze tampering traces from different image regions. Furthermore, a flexible JPEG compressed artifact learning module is designed to capture the image noise caused by JPEG compression, in which the weights can be adjusted flexibly based on image quality, without the need for decompression operations on JPEG images. Our proposed method can significantly strength the differentiation capability of detection model for tampered and non-tampered regions. A series of experiments are performed over different image sets, and the results demonstrate that the proposed scheme can achieve an overall localization performance for multi-scale JPEG manipulation regions and outperform most of state-of-the-art schemes in terms of detection accuracy, generalization and robustness.

SSAT: Active Authorization Control and User’s Fingerprint Tracking Framework for DNN IP Protection

As training a high-performance deep neural network (DNN) model requires a large amount of data, powerful computing resources and expert knowledge, protecting well-trained DNN models from intellectual property (IP) infringement has raised serious concerns in recent years. Most existing methods using DNN watermarks to verify the ownership of the models after IP infringement occurs, which is reactive in the sense that they cannot prevent unauthorized users from using the model in the first place. Different from these methods, in this article, we propose an active authorization control and user’s fingerprint tracking method for the IP protection of DNN models by utilizing sample-specific backdoor attack. The proposed method inversely and multiplely exploits sample-specific trigger as the key to implement authorization control for DNN model, in which the generated triggers are imperceptible and sample-specific for clean images. Specifically, a U-Net model is used to generate backdoor instances. Then, the target model is trained on the clean images and backdoor instances, which are inversely labeled as wrong classes and correct classes, respectively. Only authorized users can use the target model normally by pre-processing the clean images through the U-Net model. Moreover, the images processed by the U-Net model will contain unique fingerprint that can be extracted to verify and track the corresponding user’s identity. This article is the first work that utilizes the sample-specific backdoor attack to implement active authorization control and user’s fingerprint management for DNN model under black-box scenarios. Extensive experimental results on ImageNet dataset and YouTube Aligned Face dataset demonstrate that the proposed method is effective in protecting the DNN model from unauthorized usage. Specifically, the protected model has a low inference accuracy (1.00%) for unauthorized users, while maintaining a normal inference accuracy (97.67%) for authorized users. Besides, the proposed method can achieve 100% fingerprint tracking success rates on both the ImageNet and YouTube Aligned Face datasets. Moreover, it is demonstrated that the proposed method is robust against fine-tuning attack, pruning attack, pruning attack with retraining, reverse-engineering attack, adaptive attack, and JPEG compression attack. The code is available at https://github.com/nuaaaisec/SSAT .

Implementation of Robust and Secure Watermarking Algorithm on FPGA using DCT

Digital watermarking deals with embedding digital content in a cover signal so that it becomes indiscernible to make it robust against different security threats. This work demonstrates the implementation of a robust and secure watermarking technique on a Field Programmable Gate Array (FPGA) using Discrete Cosine Transform (DCT). Block by block DCT of the cover image was computed and watermark pixels in each of the blocks were hidden using middle-frequency band coefficients. Security is ensured by encrypting the watermarkwith a secret key and Arnold transform. The resultant watermark image is robust to various threats like JPEG compression, scaling, cropping, and noise attacks. This watermarking approach requires a large amount of data to be processed at high speed. FPGA is the best choice for such an application. Also, it is reprogrammable and easily upgradable according to user application. In this work, a watermarking algorithm is implemented using Xilinx design tools on the Artix-7 FPGA board.

Quantum color image watermarking scheme based on quantum discrete cosine transform

Quantum watermarking is a technique that embeds specific information into a quantum carrier, used for digital copyright protection. This paper introduces a new quantum color image watermarking method that combines Quantum LOG (QLOG) edge detection with Quantum Discrete Cosine Transform (QDCT) technology. First, edge detection is used on the RGB channels of the quantum color image. This helps identify the best channel for embedding the watermark. Next, the image is decomposed using the quantum Haar wavelet transform (QHWT). The image is then processed with QDCT, selecting the mid-frequency parts for watermark embedding. Using QDCT improves the watermark’s resistance to JPEG compression. The invisibility of the watermark is verified by assessing its Peak Signal-to-Noise Ratio (PSNR) and histogram. Comparative analysis shows strong resistance to various noise attacks. Simulation results confirm that the watermarking technique maintains high visual quality and resists different adversarial attacks.

Application of the KARP RABIN Algorithm for Plagiarism Detection System in Thesis Proposal Submission in the Department of Informatics Engineering STMIK Kaputama

Plagiarism is a serious threat, especially to academic honesty, so a detection system that can analyze various types of documents is needed. This research develops a plagiarism detection system using Optical Character Recognition (OCR) to convert image text into digital text. Rabin – Karp algorithm with rolling hash and Dice Coefficient Similarity is applied to measure similarities between documents. Testing is carried out on .doc, .txt, .jpg files. As a result, the system can detect plagiarism well in clear text and image documents, but accuracy can decrease in low-quality images. In conclusion, the similarity of content, sentence structure, and format affects the degree of similarity, while OCR techniques work effectively even though they are limited to low-quality images.

Super Encryption Feal Algorithm and Base64 Algorithm Image File Security

In the rapidly advancing digital era, image file security has become a critical issue, especially with the increasing risks of data breaches and attacks on digital files. This study aims to enhance the security of image files by implementing a combination of two cryptographic algorithms: Fast Data Encipherment Algorithm- 4 (FEAL-4) and Base64. FEAL-4 is a symmetric encryption algorithm known for its high speed and processing efficiency, while Base64 is used for encoding binary data into ASCII format to ensure safer transmission. This research develops a super encryption system that integrates these two algorithms to protect the integrity and confidentiality of image files, particularly for BMP, JPEG, and PNG formats. The implementation was carried out using the Visual Basic programming language. The results of the study show that the combination of FEAL-4 and Base64 algorithms significantly enhances the security of image files, with a high success rate in the encryption and decryption processes.