Joint Photographic Experts Group Images Research Articles

Domain Transformation of Distortion Costs for Efficient JPEG Steganography with Symmetric Embedding

Nowadays, most image steganographic schemes embed secret messages by minimizing a well-designed distortion cost function for the corresponding domain, i.e., the spatial domain for spatial image steganography or the JPEG (Joint Photographic Experts Group) domain for JPEG image steganography. In this paper, we break the boundary between these two types of schemes by establishing a theoretical link between the distortion costs in the spatial domain and those in the JPEG domain and thus propose a scheme for domain transformations of distortion costs for efficient JPEG steganography with symmetric embedding, which can directly convert the spatial distortion cost into its JPEG counterpart. Specifically, by formulating the distortion cost function for JPEG images in the decompressed spatial domain, a closed-form expression for a distortion cost cross-domain transformation is derived theoretically, which precisely characterizes the conversion from the distortion costs obtained by existing spatial steganographic schemes to those applied in JPEG steganography. Experimental results demonstrate that the proposed method outperforms other advanced JPEG steganographic schemes, e.g., JUNIWARD (JPEG steganography with Universal Wavelet Relative Distortion), JMiPOD (JPEG steganography by Minimizing the Power of the Optimal Detector), and DCDT (Distortion Cost Domain Transformation), in resisting the detection of various advanced steganalyzers.

Reversible Data Hiding for JPEG Images With Adaptive Multiple Two-Dimensional Histogram and Mapping Generation

Reversible data hiding based on joint photographic experts group (JPEG) images has been extensively studied to enhance embedding performance in terms of visual quality and file size preservation at the desired payload. In this paper, an efficient adaptive RDH method for JPEG images with multiple two-dimensional (2D) histogram modification is proposed. Firstly, the proposed method proposes the block smoothness estimator and the band smoothness estimator, and then combines the two estimators to reduce the embedding distortion as much as possible at the desired payload. Instead of adopting a fixed 2D mapping or choosing one from several empirically-designed mappings for each 2D histogram, the proposed method designs an adaptive 2D mapping generation strategy to adaptively generate a large number of mappings with considering the local characteristics of histogram distribution. Since exhaustively searching for the optimal mapping achieving the highest embedding performance for each 2D histogram is time-consuming, an improved discrete particle swarm optimization is utilized in the proposed method to speed up the optimization process. Extensive experimental results also demonstrate the effectiveness of the proposed method in terms of visual quality and file size increment of the stego image.

Digital Image Anti-Forensic Model Using Exponential Chaotic Biogeography-Based Optimization Algorithm

Abstract The innovation in visual imagery has led to massive growth in technologies, wherein digital cameras are obtainable at affordable prices. Thus, the digital images are easily captured and processed due to the internet connectivity. On the other hand, the development of strong image editing software facilitated the forgers to manipulate the accessible images with different tampering operations. Several techniques are devised for detecting the forgeries. Accordingly, this paper devises an anti-forensic model, namely Exponentially Weighted Moving Average-Chaotic Biography Based Optimization (E-CBBO) for joint photographic experts group (JPEG) compression to mitigate the forgeries occurred on the internet while transmitting data. The proposed E-CBBO is designed by integrating the properties of the exponentially weighted moving average (EWMA) with the chaotic biography-based optimization (CBBO). The suggested JPEG anti-forensic model is used to eliminate JPEG compression artifacts through the use of unique deblocking, smoothing with dither and decalibration operations. In addition, the goal is to balance visual quality and forensic undetectability when compressing the JPEG image. The fitness function is developed using the structural similarity index (SSIM), universal image quality index (UIQI) and histogram deviation parameters. With a maximum accuracy of 93.2%, a minimal MSE of 0.110, a maximum SSIM of 0.932 and a maximum UIQI of 0.890, the suggested E-CBBO beat existing approaches.

Detecting Aligned Double JPEG Compressed Color Image With Same Quantization Matrix Based on the Stability of Image

Joint photographic experts group (JPEG) compression is widely used in image processing and computer vision. Detecting double compressed JPEG images is a common problem in forensics and detecting compressed images with the same quantization matrix remains a challenging task. However, most existing methods were designed for detection in grayscale images and cannot fully use the unique characteristics of color images (such as the relationship between channels and color information). In addition, the performance of existing methods is unsatisfactory for low JPEG quality factors and in cross detection experiments. To solve these problems, we analyze the stability of a color image to obtain the convergence error and transposition error. According to the convergence characteristics of color JPEG images, the continuous compression by the same quantization matrix can make the JPEG image tend to be stable. The final stable state and the convergence process are determined by the number of compressions of the original image. Thus, continuously compressed JPEG images can be regarded as a continuous frame to obtain the convergence error. As the color image converges, its ability to resist interference decreases. To reflect the changes in anti-interference ability, the transposition operation is used to disturb the color JPEG image to obtain the transposition error. In addition, quaternion mapping is used to retain the relationship between continuously compressed JPEG images and enlarge the influence caused by transposition operation. In our experiments on several image databases, the proposed method outperforms existing methods in different settings.

Reversible data hiding in JPEG images based on coefficient-first selection

Joint photographic experts group (JPEG) is the most commonly used image format on the Internet, and using reversible data hiding (RDH) technology to verify its authenticity and integrity is of great significance. So far, many RDH algorithms for JPEG image have been proposed, which improve the performance of the algorithm via block sorting, frequency selection and other adaptive strategies. In this paper, a new JPEG RDH method based on coefficient-first selection strategy is proposed. In our new method, the distribution models of alternating current (AC) coefficients belonging to different discrete cosine transform (DCT) coefficient blocks are constructed first, and then the distortion value of each AC coefficient can be calculated through the estimated probability density function of the constructed coefficient model. In the embedding process, the coefficients with low distortion values will be preferentially selected for data hiding. Various experimental results demonstrate that compared with the state-of-the-art JPEG RDH methods, our proposed may have better visual quality and less increase of file storage size.

Adaptive reversible data hiding for JPEG images with multiple two-dimensional histograms

Joint photographic experts group (JPEG) can provide good quality with small file size but also eliminate extensively the redundancies of images. Therefore, hiding data into JPEG images in terms of maintaining high visual quality at small file sizes has been a great challenge for researchers. In this paper, an adaptive reversible data hiding method for JPEG images containing multiple two-dimensional (2D) histograms is proposed. Adaptability is mainly reflected in three aspects. The first one is to preferentially select sharper histograms for data embedding after K histograms are established by constructing the kth (k∈{1,2,…,K}) histogram using the kth non-zero alternating current (AC) coefficient of all the quantized discrete cosine transform blocks. On the other hand, to fully exploit the strong correlation between coefficients of one histogram, the smoothness of each coefficient is estimated by a block smoothness estimator so that a sharply-distributed 2D-histogram is constructed by combining two coefficients with similar smoothness into a pair. The pair corresponding to low complexity is selected priorly for data embedding, leading to high embedding performance while maintaining low file size. Besides, we design multiple embedding strategies to adaptively select the embedding strategy for each 2D histogram. Experimental results demonstrate that the proposed method can achieve higher rate–distortion performance which maintaining lower file storage space, compared with previous studies.

A Feature-Enriched Deep Convolutional Neural Network for JPEG Image Compression Artifacts Reduction and its Applications.

The amount of multimedia data, such as images and videos, has been increasing rapidly with the development of various imaging devices and the Internet, bringing more stress and challenges to information storage and transmission. The redundancy in images can be reduced to decrease data size via lossy compression, such as the most widely used standard Joint Photographic Experts Group (JPEG). However, the decompressed images generally suffer from various artifacts (e.g., blocking, banding, ringing, and blurring) due to the loss of information, especially at high compression ratios. This article presents a feature-enriched deep convolutional neural network for compression artifacts reduction (FeCarNet, for short). Taking the dense network as the backbone, FeCarNet enriches features to gain valuable information via introducing multi-scale dilated convolutions, along with the efficient 1 ×1 convolution for lowering both parameter complexity and computation cost. Meanwhile, to make full use of different levels of features in FeCarNet, a fusion block that consists of attention-based channel recalibration and dimension reduction is developed for local and global feature fusion. Furthermore, short and long residual connections both in the feature and pixel domains are combined to build a multi-level residual structure, thereby benefiting the network training and performance. In addition, aiming at reducing computation complexity further, pixel-shuffle-based image downsampling and upsampling layers are, respectively, arranged at the head and tail of the FeCarNet, which also enlarges the receptive field of the whole network. Experimental results show the superiority of FeCarNet over state-of-the-art compression artifacts reduction approaches in terms of both restoration capacity and model complexity. The applications of FeCarNet on several computer vision tasks, including image deblurring, edge detection, image segmentation, and object detection, demonstrate the effectiveness of FeCarNet further.

Blind robust image watermarking based on adaptive embedding strength and distribution of quantified coefficients

The image watermarking technique is a reliable method for protecting digital image rights. The most widely used digital imaging format is the JPEG (joint photographic expert’s group). This paper proposes a novel reversible robust watermarking scheme based on the JPEG image. In JPEG image, quantified Discrete Cosine Transform (DCT) coefficients with varying frequency provide varied capacities and embedding deformities. To minimize total distortion for the watermarked image, we adaptively select embedding strengths and coefficients from effective frequency selection that produce less embedding distortions. Then, by weighting the difference between the quantified DCT coefficient and the DCT coefficient on the block, an advanced block selection strategy is used to the corresponding distortion, which are produced from the whole quantified alternating current (AC) shift capacity and half of the insertion capacity. The experimental results show that the proposed method can maintain good visual quality while requiring little bitstream expansion and is robust to geometrical and image processing attacks.

Comparative Study on MATLAB based Joint Photographic Experts Group Image Size Reduction Using Shearlet and Wavelet Packet Transform for X-ray Images with Potential Hospital Data Storage Applications

Aim: Aim of this study is to compress X-ray images using wavelet packet transform and shearlet transform. These two transforms were compared for the better compression ratio to store large hospital data files in small memory space. Materials and methods: Sample X-ray images were, Wavelet packet (30) and Shearlet (30) for compression. Compression ratio was calculated. The significance of the data were calculated using SPSS software. Results: There was statistical significance between the wavelet packet and shearlet transform based compression ratio data (p<0.05, independent sample t- test). Conclusion: wavelet packet transform has a better compression ratio than shearlet transform. Wavelet packet transform appears to produce the most consistent results with higher standard deviation (3.3) when compared with shearlet transform which appears to produce lower results with lower standard deviation (0.8).

Comparative Study on MATLAB based Joint Photographic Experts Group Image Size Reduction Using Shearlet and Wavelet Transform for X-Ray Images with Potential Hospital Data Storage Application

Aim: The aim of this paper is to compress the x-ray images using wavelet and shearlet transform and compare the compression ratios to find a better transform. Materials and Methods: The sample size of each group was 30 and the total sample size was 60. Compression ratios (CR) were generated for images based on the transform used for size reduction. Results: Wavelet based CR was seen to be higher mean (6.61) when compared to shearlet (4.025). Hence wavelet transform seems better transform for compressing X-Ray images. Conclusion: We observed that wavelet transform seems to have a better compression ratio compared to shearlet transform (P=0.28, P<0.05, independent sample t-test).

Improved JPEG image watermarking in data compression domain using block selection strategy

Nowadays, the joint photographic experts group (JPEG) is still the most dominant format among various digital image formats used in our daily life. Therefore, watermarking JPEG images is important and useful for many applications such as archive management and image authentication. Along with the embedding capacity and visual quality that must be considered for uncompressed images, the storage size of the watermarked JPEG image should be also considered. In this paper, based on the philosophy behind the JPEG encoder and the statistical properties of discrete cosine transform (DCT) coefficients, we propose a new JPEG image watermarking in the data compression domain, in which only coefficients with values 1 and -1 are expanded to carry message bits, coefficients with values 2 and -2 are shifted, and all the others coefficients remain unchanged. Moreover, a block selection strategy based on the number of 1 and -1 coefficients in each 8×8 block is proposed, which can be utilized to adaptively choose DCT coefficients for data embedding. Experimental results demonstrate that by using the proposed method, we can easily realize high embedding capacity and good visual quality. Furthermore, the storage size increase for the watermarked JPEG image is well lessened.

SAE based unified double JPEG compression detection system for Web image forensics

PurposeRecently, the double joint photographic experts group (JPEG) compression detection tasks have been paid much more attention in the field of Web image forensics. Although there are several useful methods proposed for double JPEG compression detection when the quantization matrices are different in the primary and secondary compression processes, it is still a difficult problem when the quantization matrices are the same. Moreover, those methods for the different or the same quantization matrices are implemented in independent ways. The paper aims to build a new unified framework for detecting the doubly JPEG compression.Design/methodology/approachFirst, the Y channel of JPEG images is cut into 8 × 8 nonoverlapping blocks, and two groups of features that characterize the artifacts caused by doubly JPEG compression with the same and the different quantization matrices are extracted on those blocks. Then, the Riemannian manifold learning is applied for dimensionality reduction while preserving the local intrinsic structure of the features. Finally, a deep stack autoencoder network with seven layers is designed to detect the doubly JPEG compression.FindingsExperimental results with different quality factors have shown that the proposed approach performs much better than the state-of-the-art approaches.Practical implicationsTo verify the integrity and authenticity of Web images, the research of double JPEG compression detection is increasingly paid more attentions.Originality/valueThis paper aims to propose a unified framework to detect the double JPEG compression in the scenario whether the quantization matrix is different or not, which means this approach can be applied in more practical Web forensics tasks.

Medical JPEG image steganography method according to the distortion reduction criterion based on an imperialist competitive algorithm

In most of the digital steganography methods provided for natural digital images, the embedding of the confidential message is based on the minimisation of the defined distortion functions. It is often done based on choosing the most optimal criterion of distortion. Although the distortion functions are designed innovatively, steganography algorithms will be optimal. In such approaches, embedding interactions are often overlooked. Unlike usual images that have areas with a variety of tissue features, there are many smooth areas in medical images that will make the changes more visible if they are manipulated. Therefore, this study presents an adaptive approach that comes from the interactions between the changes made during the embedding algorithm to reduce the probability of recognising the message embedded in medical images and reducing the distortion caused by embedding in a discrete cosine transform space and based on the imperialist competitive algorithm for joint photographic experts group images, especially in medical images due to the importance of information steganography in them. The results obtained show the high efficiency of the proposed algorithm in comparison with the state-of-the-art methods that are presented in this area.

Quality Factor terhadap Kapasitas Pesan Rahasia pada Steganografi Citra JPEG dan Kualitas Citra Stego

One of the container media that is available and popular is the Joint Photographic Experts Group (JPEG) format image. This article aims to determine the effect of Quality Factor on the secret message capacity of JPEG image steganography and stego image quality. The quality of an image can actually be seen subjectively with the human eye, but this is relative between each individual. Because the assessment of the human eye varies from person to person. In addition, the effect of Quality Factor on secret message capacity is not yet known whether it has an impact. Therefore, in this study the Quality Factor is used to objectively see the secret message capacity of the JPEG image steganography and the quality of the stego image. The parameter used to determine the quality of an image is the Peak Signal to Noise Ratio (PSNR). PSNR will compare the quality of the original image (before steganography) with the stego image. The test results show that the Q Factor effect can affect the secret message capacity of the JPEG image steganography and the stego image quality. The bigger the Q Factor, the more the message capacity is generated. The greater the Q factor, the better the quality of the resulting stego image.

Stego key recovery method for F5 steganography with matrix encoding

When embedding secret message into image by steganography with matrix encoding, there are still no effective methods to recover the stego key because it is difficult to statistically distinguish the stego coefficient sequences selected by true and false keys. Therefore, this paper proposes a method for recovering the stego key of a typical JPEG (Joint Photographic Experts Group) image steganography—F5 which composes of the check matrix and shuffling key. Firstly, the check matrix is recovered based on the embedding ratio estimated by quantitative steganalysis. The shuffling key is then recovered based on the distribution difference between the bit sequences extracted by the true and false shuffling keys. Additionally, the cardinality of the shuffling key space is significantly reduced by examining the extracted encoding parameter and message length. Experimental results show that the proposed method can recover the stego key accurately and efficiently, even when the existing Xu’s method fails for the high or very low embedding ratio.

Reversible data hiding for JPEG images based on pairwise nonzero AC coefficient expansion

JPEG (Joint Photographic Experts Group) is the most common format in our daily life for storing and transmitting photographic images. Reversible data hiding (RDH) for JPEG images currently is one of the hotspots in the field of data hiding. However, different from RDH in uncompressed images, any modification in the compressed domain may produce distinct visual distortion and significantly increase the file size of the marked image. In this paper, a new JPEG RDH scheme based on pairwise nonzero AC coefficient expansion (pairwise NACE) is proposed. Firstly, by taking every two adjacent nonzero alternating current (AC) coefficients together to generate the nonzero-AC-coefficient pairs (NACPs), a sharp two-dimensional (2D) histogram can be constructed. Then a new reversible 2D mapping is carefully designed, and data are embedded based on the modification of these NACPs according to the mapping. Furthermore, an adaptive embedding strategy based on block and DCT frequency selection is proposed in order to preserve the visual quality and reduce the file size increase of the marked JPEG image. Experimental results demonstrate that our proposed method can achieve better performance both in visual quality and file size preservation compared to the previous works.

Reversible data hiding in JPEG images based on zero coefficients and distortion cost function

Recently, reversible data hiding (RDH) in joint photographic experts group (JPEG) images has received a great deal of attention since the JPEG image is one of the most popularly used image formats nowadays. Generally in JPEG image, the quantized discrete cosine transform (DCT) coefficients with the value of zero are far more than those with nonzero coefficients. However, most existing methods are avoided to change these zero coefficients for fear of JPEG file size increase, and the designed distortion cost functions are not perfect. In this paper, we propose a new JPEG RDH method in which the numerous zero coefficients can be modified. Based on an improved distortion cost function, a strategy which can measure the distortion for each coefficient and select coefficient positions which are most suitable for embedding is proposed. With this strategy, zero coefficients are applied to data embedding and the embedding performance is improved. Experimental results have proved that the proposed method can effectively reduce the embedding distortion and increase the embedding capacity while maintaining a relative good file size.

DBFS: Dragonfly Bayes Fusion System to detect the tampered JPEG image for forensic analysis

Due to the advancement of a variety of photo editing and image processing software, image forensics analysis has become an important research topic in recent years. Numbers of research works are presented for image forensic analysis. Accordingly, this paper proposes a method named as Dragonfly Bayes Fusion System (DBFS) by integrating the Naive Bayes (NB) classifier and the Dragonfly optimization for detecting the tampered Joint Photographic Experts Group image. Initially, the input image is applied to the existing six forensic tools separately, and the classified binary map is generated. Then, the proposed DBFS fuses these decisions for generating the optimal decision. Here, the NB classifier creates the model by finding the mean and variance of every feature and this model is given as input to the Dragonfly optimization for optimally generating the probabilistic measures. Finally, the posterior probability of each feature is determined with respect to the tampered class, and the original class and the tampered image block is determined. The performance of the proposed system is evaluated with the existing methods, such as fuzzy theory based classification, rule-based classification, average method, and weighted average method for the evaluation metrics accuracy, False Positive Rate (FPR), and True Positive Rate (TPR). The experimental results show that the proposed system outperforms the existing methods by obtaining the maximum accuracy of 0.9519, minimum FPR of 0.0490, and maximum TPR of 0.8720 when compared to the existing methods.

Impact of pre-analytical variables on deep learning accuracy in histopathology.

Machine learning (ML) binary classification in diagnostic histopathology is an area of intense investigation. Several assumptions, including training image quality/format and the number of training images required, appear to be similar in many studies irrespective of the paucity of supporting evidence. We empirically compared training image file type, training set size, and two common convolutional neural networks (CNNs) using transfer learning (ResNet50 and SqueezeNet). Thirty haematoxylin and eosin (H&E)-stained slides with carcinoma or normal tissue from three tissue types (breast, colon, and prostate) were photographed, generating 3000 partially overlapping images (1000 per tissue type). These lossless Portable Networks Graphics (PNGs) images were converted to lossy Joint Photographic Experts Group (JPG) images. Tissue type-specific binary classification ML models were developed by the use of all PNG or JPG images, and repeated with a subset of 500, 200, 100, 50, 30 and 10 images. Eleven models were generated for each tissue type, at each quantity of training images, for each file type, and for each CNN, resulting in 924 models. Internal accuracies and generalisation accuracies were compared. There was no meaningful significant difference in accuracies between PNG and JPG models. Models trained with more images did not invariably perform better. ResNet50 typically outperformed SqueezeNet. Models were generalisable within a tissue type but not across tissue types. Lossy JPG images were not inferior to lossless PNG images in our models. Large numbers of unique H&E-stained slides were not required for training optimal ML models. This reinforces the need for an evidence-based approach to best practices for histopathological ML.

Exploration of Wireless Multimedia Sensor Network Based on Joint Photographic Experts Group Image Coding Algorithm

to study the performance of image coding and image transmission of wireless video sensor node in wireless multimedia sensor network, the challenges and design objectives of image coding technology in wireless multimedia sensor network are discussed, and the classification and exploration are carried out from two aspects: individual source coding and distributed source coding. The compression of video monitoring image sequence in wireless multimedia sensor network is studied and a low complexity image coding scheme based on change detection and improved Joint Photographic Experts Group (JPEG) algorithm is proposed. The moving target in the monitoring image is located with the change detection algorithm, that is, the area of interest. Only the region of interest is transmitted to reduce the quantity of data transmission and adapt to the limited storage and forwarding capability of wireless sensor nodes; The Discrete Cosine Transform (DCT) and quantization processes in Joint Photographic Experts Group (JPEG) algorithm are improved to reduce the computational complexity and adapt to the limited computing power of wireless sensor nodes.The analysis of algorithm complexity and the results of simulation experiment show that the proposed method can effectively reduce the data communication amount of wireless sensor nodes and the energy consumption in the calculation process while ensuring the image quality required by the application.Wireless multimedia sensor network is a new and interdisciplinary subject.The study of it is also multidirectional and multi-angle.The image coding algorithm of wireless multimedia sensor network is studied deeply.Although some achievements have been made, there are still a lot of problems that need to be further studied.