AC Coefficients Research Articles

Progressive Histogram Modification for JPEG Reversible Data Hiding

With the development of social network, a large number of private JPEG images are stored in social cloud platform. Correspondingly, the platform embeds user ID or authentication labels to manage these privacy images, preventing them from being arbitrarily accessed or tampered by illegal persons. However, data embedding in JPEG domain inevitably produces irreversible modifications to DCT coefficients, thus resulting in obvious or even serious distortion in the host JPEG images. To address this problem, this paper proposes an efficient JPEG reversible data hiding (RDH) method by constructing progressive two-dimensional histogram mappings. We firstly design distortion function to calculate the cost of each DCT frequency band, and then sort them to build histogram mapping containing a series of coefficient pairs. Subsequently, a progressive mapping mechanism is introduced to maintain most of AC coefficients unchanged. According to the given capacity, this mechanism can adaptively generate an optimum two-dimensional histogram mapping to embed secret messages. Our scheme can achieve an effective balance among embedding capacity, visual quality of the marked image and file size expansion, while keeping high cost-performance complexity. Extensive experiments demonstrate that our method outperforms existing JPEG RDH schemes in terms of visual quality and file size increment of the marked image, and provides an efficient solution for the confidentiality and security access problem of sensitive private image in cloud environment.

JPEG-compatible Joint Image Compression and Encryption Algorithm with File Size Preservation

Joint image compression and encryption algorithms are intensively investigated due to their powerful capability of simultaneous image data compression and sensitive information protection. Unfortunately, most of the existing algorithms suffered from either poor compression efficiency or weak encryption strength, making them vulnerable to cryptanalysis. To address these limitations, we propose a chaos-based JPEG-compatible joint image compression and encryption algorithm. We separate the luminance and chrominance coefficients to preserve file size and encrypt the discrete cosine transform (DCT) coefficients in parallel. The proposed inter-block DC encryption strategy achieves high encryption intensity based on the permutation-substitution structure. In addition, we apply both inter- and intra-block permutations to AC coefficients and strengthen the encryption using an inter-block substitution for non-zero AC coefficients. The results of security and performance analyses demonstrate that the proposed algorithm offers robust encryption of image data while maintaining compression efficiency for real-time transmission.

JPEG image encryption with grouping coefficients based on entropy coding

In the existing JPEG image encryption schemes, the block feature values that can be used to reduce key search space are either difficult to be changed or need to be changed through overflow processing which leads to low generality and high encryption runtime. To change block feature values without overflow processing, a novel JPEG image encryption scheme is proposed. For AC encryption, the complete and end AC groups based on undivided RSV (run/size, value) (ACG-URSV) are permuted separately to change different features. Complete ACG-URSV containing different number of RSVs is used to change the non-zero coefficients count (NCC) and energy of AC coefficients (EAC). End ACG-URSV containing the zero coefficients after position of last non-zero AC coefficient (PLZ) is mainly used to change PLZ. Besides, the intra-block RSV permutation and block permutation are used to further destroy correlation. For DC encryption, the positive DC prediction error (PDC-PE) groups modulo encryption is proposed to avoid overflow processing. The experimental results show that this paper reduces encryption runtime by more than half, improves generality by at least 20%. When quality factor of 90, the average change rates of NCC, EAC and PLZ values are increased by 96.28%, 11.68% and 29.15%, respectively.

A Joint Encryption and Compression Algorithm for Multiband Remote Sensing Image Transmission.

Due to the increasing capabilities of cybercriminals and the vast quantity of sensitive data, it is necessary to protect remote sensing images during data transmission with "Belt and Road" countries. Joint image compression and encryption techniques exhibit reliability and cost-effectiveness for data transmission. However, the existing methods for multiband remote sensing images have limitations, such as extensive preprocessing times, incompatibility with multiple bands, and insufficient security. To address the aforementioned issues, we propose a joint encryption and compression algorithm (JECA) for multiband remote sensing images, including a preprocessing encryption stage, crypto-compression stage, and decoding stage. In the first stage, multiple bands from an input image can be spliced together in order from left to right to generate a grayscale image, which is then scrambled at the block level by a chaotic system. In the second stage, we encrypt the DC coefficient and AC coefficient. In the final stage, we first decrypt the DC coefficient and AC coefficient, and then restore the out-of-order block through the chaotic system to get the correct grayscale image. Finally, we postprocess the grayscale image and reconstruct it into a remote sensing image. The experimental results show that JECA can reduce the preprocessing time of the sender by 50% compared to existing joint encryption and compression methods. It is also compatible with multiband remote sensing images. Furthermore, JECA improves security while maintaining the same compression ratio as existing methods, especially in terms of visual security and key sensitivity.

Robust Texture-Aware Local Adaptive Image Watermarking With Perceptual Guarantee

Watermarking involves embedding a watermark in an image and later extracting it to prove the image’s copyright. In most cases, a complete image contains both smooth and textured regions. As a rule of thumb, the visual quality of an image with a watermark embedded in its textured regions is better than that of the same image with a watermark in smooth regions. This paper, by taking advantage of the fact, proposes a texture-aware local adaptive watermarking algorithm to maximize the watermark’s robustness while maintaining its imperceptibility. To identify textured regions in an image, we introduce the texture value, an efficient and proper metric of the richness of image texture. It combines the texture correlation of the AC coefficients, the luminance masking of the DC coefficient, and the distribution of image texture. A watermark is embedded adaptively into multiple non-overlapping textured regions of an image under the specified SSIM condition. Its adaptiveness comes from a novel texture-aware adaptive parameter model derived by multivariate regression analysis. Correct extraction of watermarks from multiple textured regions can be done by the cooperation of embedding and extraction strategies, with the assistance of RS-based watermark coding model. They allow for greater robustness, faster extraction, and adjustable watermark capacity. The simulation experiments on 100 images demonstrate that our proposed algorithm outperforms state-of-the-art algorithms with respect to imperceptibility, robustness, and adaptability.

A DCT probability histogram-based ROI features for content-based natural and medical image retrieval applications

The effectiveness of a Content-based Image Retrieval (CBIR) system depends on selecting representative and salient visual features. Capturing the salient image features for forming a feature vector is an essential part of the CBIR process, as it can significantly affect retrieval efficiency. Most images contain a visually prominent region that perceives maximum image semantic information, known as Regions-of-Interest (ROIs). This paper suggests a new CBIR scheme that employs a Graph-based Visual Saliency (GBVS) map to extract the ROI of the image. Further, block-level Discrete Cosine Transforms (DCT) and subsequent histograms are computed for the DC and significant AC coefficients of the extracted ROI image. In addition, a low-dimensional feature vector is formed from the modified histograms. Subsequently, six statistical features from the background scene have also been computed for the final feature vector construction. As a result, the final feature vector contains ROI and background information in their proper proportion to improve retrieval performance. Moreover, the performance of the proposed CBIR scheme has been evaluated using one medical, three natural, two objects, one texture, and one natural heterogeneous image database. All these databases collectively contain 53732 different images from 200 image classes. The outcomes of the experiments demonstrate substantial improvement in the retrieval process with some existing related schemes. At the same time, it also shows that the performance of the proposed scheme is robust concerning all kinds of image databases.

A multi-level sorting prediction enhancement-based two-dimensional reversible data hiding algorithm for JPEG images

To improve the performance of reversible data hiding on JPEG images, a multi-level sorting prediction enhancement-based two-dimensional reversible data hiding algorithm for JPEG images is proposed in this paper. Firstly, the smoothness of each DCT block is evaluated by counting the location of the last non-zero coefficient in each DCT block. Then, the gradient of the object block is calculated with the DC coefficients of its surrounding blocks, and the lowest gradient direction blocks are employed to achieve the prediction of object block AC coefficients. Secondly, these gradient of adjacent blocks of the object block are compared to found the lowest gradient direction. The AC coefficient of the object block is accurately predicted by coefficients at the same position of its lowest gradient-direction adjacent blocks. Thirdly, the obtained DCT coefficient prediction errors are paired with a similar prediction-error pairing strategy to construct a two-dimensional prediction-error plane, and the secret data is imperceptibly embedded into the AC coefficients by using the two-dimensional histogram shifting algorithm. The performance of the proposed algorithm is evaluated on different datasets, and six representative images are involved in demonstrating its superiority. Extensive experimental results show that, compared to other state-of-the-art reversible data hiding algorithms, the proposed algorithm can achieve higher visual quality of the data-embedded image while maintaining low JPEG image file size increment.

JPEG Reversible Data Hiding Using Dynamic Distortion Optimizing With Frequency Priority Reassignment

JPEG Reversible data hiding (RDH) aims at correctly extracting hidden data and perfectly recovering the original JPEG image. By combining different frequency selection and block ordering manners, appropriate AC coefficients can be determined to achieve an efficient JPEG RDH scheme using histogram shifting (HS) mechanism. Nevertheless, existing methods only rely on rough frequency band distortion model by involving all DCT blocks, ignoring the dynamic influence of block ordering on embedding block selection. As a result, JPEG image containing hidden data may have an obvious distortion and file size expansion due to an inappropriate frequency band selection. To address the aforementioned problem, we design a new JPEG RDH scheme to optimize distortion model with frequency priority reassignment. Our method firstly orders all DCT blocks by referring to zero AC coefficients of each block. Subsequently, based on the given secret data, only partial ordered blocks are selected to calculate the distortion of each DCT frequency, which can further optimize unit distortion caused by shiftable coefficients. Furthermore, we employ the adjustment parameter to adaptively reassign the priority of each frequency band based on its influence on file size expansion, which can significantly reduce file size expansion of the marked JPEG image. Extensive experiments demonstrate that our method outperforms existing JPEG RDH methods with better visual quality and smaller file size increment of the marked image.

Pediatric Patient and Caregiver Agreement on Perioperative Expectations and Self-Reported Outcomes

IntroductionAlignment between pediatric patients and caregiver perspectives on patient-reported outcome (PRO) data is contingent upon context. We aimed to assess agreement between patient and caregiver responses to a series of perioperative domains. MethodsAgreement between pediatric patients and caregiver responses to preoperative and postoperative surveys about surgery preparedness, perioperative expectations, PRO Measurement Information System (PROMIS) measures for overall health and pain, and reaching milestones gathered as part of an ongoing clinical trial for children undergoing gastrointestinal surgery, was evaluated. Gwet's AC and Spearman's correlation coefficients were calculated, as appropriate, to assess agreement. ResultsOf 209 enrolled patients, 65 (31.1%) dyads completed all three surveys and were included. For the domains of education, expectations, and comprehension, patients and caregivers had good agreement with Gwet AC1 with values of 0.80, 0.61, and 0.64, respectively. For milestones, patients and caregivers had very good agreement (Gwet AC1 of 0.95). Milestones measured whether patients achieved certain goals within a prespecified time, including enteral intake (Gwet AC1 0.91 and 0.92 respectively), transition to oral pain medication (Gwet AC1 0.94), ambulation (Gwet AC1 1.00), and return of bowel function (Gwet AC1 0.97). There was moderate to strong agreement between patients and caregivers on PROMIS pain questions (Spearman's correlation: 0.71 preoperatively and 0.51 postoperatively). On PROMIS global health questions, there was strong agreement (0.69 preoperatively and 0.65 postoperatively). ConclusionsPediatric patient and caregiver agreement on perioperative survey items ranged from moderate to strong. Caregivers’ responses may be acceptable when some patient-level responses are not available.

Smartphone Camera Identification from Low-Mid Frequency DCT Coefficients of Dark Images.

Camera sensor identification can have numerous forensics and authentication applications. In this work, we follow an identification methodology for smartphone camera sensors using properties of the Dark Signal Nonuniformity (DSNU) in the collected images. This requires taking dark pictures, which the users can easily do by keeping the phone against their palm, and has already been proposed by various works. From such pictures, we extract low and mid frequency AC coefficients from the DCT (Discrete Cosine Transform) and classify the data with the help of machine learning techniques. Traditional algorithms such as KNN (K-Nearest Neighbor) give reasonable results in the classification, but we obtain the best results with a wide neural network, which, despite its simplicity, surpassed even a more complex network architecture that we tried. Our analysis showed that the blue channel provided the best separation, which is in contrast to previous works that have recommended the green channel for its higher encoding power.

Reversible data hiding for JPEG images based on improved mapping and frequency ordering

Due to the good balance between file size and visual quality, JPEG images are widely used in our daily lives. Many reversible data hiding (RDH) schemes have been developed for JPEG images, most of which are based on the modification of one-dimensional (1D) histograms of DCT coefficients. Recently, two-dimensional (2D) histograms of DCT coefficients are used to improve embedding efficiency. However, the existing coefficient pairing, coefficient pair mapping and embedding order can be further optimized. In this paper, a new RDH scheme for JPEG images based on 2D histogram modification is proposed. First, according to the statistical distribution of the AC coefficients, two non-zero coefficients with the same frequency in adjacent blocks, instead of two neighboring coefficients within the same block, are paired. More coefficient pairs with higher embedding efficiency can be obtained. Then, an optimized coefficient pair mapping is presented, more specifically, the coefficient pairs that occur more frequently are given priority to use the transfer mode with higher embedding efficiency. Lastly, a new method is proposed to accurately estimate the embedding efficiency of different frequencies. The data is embedded into the coefficient pairs at the frequency position where the embedding efficiency is higher. Extensive experiments on several commonly-used images and two popular image databases show that our proposed scheme can achieve better visual quality and smaller file size increments compared to the state-of-the-art RDH schemes for JPEG images.

Frequency-domain blind quality assessment of blurred and blocking-artefact images using Gaussian Process Regression model

Most of the standard image and video codecs are block-based and depending upon compression ratio the compressed images/videos suffer from different distortions. At low compression ratios, blurriness is observed and as compression increases blocking artefacts occur. Generally, in order to reduce blockiness, images are low-pass filtered which leads to more blurriness. Also, in bokeh mode images they are commonly observed: blurriness as a result of intentional blurred background while blocking artefact and global blurriness arising due to compression. Therefore, such visual media suffer from both blockiness and blurriness distortions. Along with this, noise is also a commonly encountered distortion. Most of the existing works on quality assessment quantify these distortions individually. This paper proposes a methodology to blindly measure overall quality of an image suffering from these distortions, individually as well as jointly. This is achieved by considering the sum of absolute values of low and high-frequency Discrete Frequency Transform (DFT) coefficients defined as sum magnitudes. The number of blocks lying in specific ranges of sum magnitudes including zero-valued AC coefficients and mean of 100 maximum and 100 minimum values of these sum magnitudes are used as feature vectors. These features are fed to the Machine Learning (ML) based Gaussian Process Regression (GPR) model, which quantifies the image quality. The simulation results show that the proposed method can estimate the quality of images distorted with the blockiness, blurriness, noise and their combinations. It is relatively fast compared to many state-of-art methods, and therefore is suitable for real-time quality monitoring applications.

A novel Image Security Protection Method Based on DCT Compression Theory and Hyper-chaotic Mapping

This paper studies the current situation of image compression encryption and analyzes the influence of low frequency (DC coefficient) and high frequency (AC coefficient) on image structure in DCT transformation. Based on this, a novel image security protection method based on DCT compression theory and hyper-chaotic mapping is proposed. First, the position of the pixel of the original image is disturbed, and converts the image from spatial domain into frequency domain by the two-dimensional DCT transformation and quantization. Second, change the pixel values by modifying the values of the sign bit of AC coefficient and DC coefficient. At last, the encrypted image is obtained by carrying out inverse quantization, inverse transformation and reverse operation by bit.

Design of the Poster Image System Based on Human Vision

At present, the human visual perception system is the most effective, accurate, and fast image processing system in the world. This is because human eyes have some special visual features, but the features closely related to image enhancement include color constancy and brightness constancy. This paper presents a new image enhancement framework and computational model which can better simulate human visual features. It is based on the analysis of color constancy and luminance constancy and Retinex theory. And, this is a new image enhancement method in the compressed domain based on Retinex theory. In Retinex theory, DCT coefficients consist of incident components (DC coefficients) and reflection components (AC coefficients). By adjusting the dynamic range of DC coefficients, carefully adjusting AC coefficients, and using the threshold method for block suppression, the compressed domain image can be enhanced. On the basis of Retinex theory, the incident light and reflected light components are considered synthetically, the dynamic range (DC coefficient) of the incident light component and the details of the reflected light component (AC coefficient) are adjusted, and then the incident light component is reexamined. Moreover, it achieves a better image enhancement effect and avoids the blocking effect.

Fighting Deepfakes by Detecting GAN DCT Anomalies.

To properly contrast the Deepfake phenomenon the need to design new Deepfake detection algorithms arises; the misuse of this formidable A.I. technology brings serious consequences in the private life of every involved person. State-of-the-art proliferates with solutions using deep neural networks to detect a fake multimedia content but unfortunately these algorithms appear to be neither generalizable nor explainable. However, traces left by Generative Adversarial Network (GAN) engines during the creation of the Deepfakes can be detected by analyzing ad-hoc frequencies. For this reason, in this paper we propose a new pipeline able to detect the so-called GAN Specific Frequencies (GSF) representing a unique fingerprint of the different generative architectures. By employing Discrete Cosine Transform (DCT), anomalous frequencies were detected. The statistics inferred by the AC coefficients distribution have been the key to recognize GAN-engine generated data. Robustness tests were also carried out in order to demonstrate the effectiveness of the technique using different attacks on images such as JPEG Compression, mirroring, rotation, scaling, addition of random sized rectangles. Experiments demonstrated that the method is innovative, exceeds the state of the art and also give many insights in terms of explainability.

Spatial and Frequency Approaches for Audio File Protection

In order to protect audio files, we propose in this paper a new integration scheme for blind audio file watermarking. The goal is to find a compromise between capacity and imperceptibility in order to hide as much data as possible while minimizing file degradation. This integration scheme is implemented in the three insertion domains: spatial, frequency and multi-resolution domains. For the spatial-domain integration, the mark is inserted directly into the data samples. For the frequency-domain integration, a Discrete Cosine Transform is applied to the audio frames; after the thresholding and quantification step, the watermark is inserted into the Discrete Cosine Transform coefficients to obtain the watermarked file. For the multi-resolution-domain insertion, a single-level Discrete Wavelet Transform is applied using the scaling low-pass filter and wavelet high-pass filter. The watermark integration is then performed using the obtained AC coefficients. The proposed concealment process combines three values to integrate two bits and only one may be modified, which reduces the probability of change unlike other approaches. This implies less modification and therefore less distortion of the host file; this explains the good Signal-to-Noise Ratio obtained of more than 59[Formula: see text]dB for the spatial-domain integration and therefore a reasonable imperceptibility. An evaluation of the watermark’s robustness demonstrates that the proposed schemes generate reasonably robust watermarked samples against various attacks with a high-quality watermark with normalized cross-correlation greater than 0.9 for the three insertion domains.

Artificial bee colony based perceptually tuned blind color image watermarking in hybrid LWT-DCT domain

A perceptually tuned blind digital watermarking method for color images in a hybrid lifting wavelet transform (LWT) and discrete cosine transform (DCT) domain is proposed in this work. The color watermark is scrambled by arnold transform before embedding to enhance the security level. The color host image is firstly transformed by lifting wavelet transform and the approximate sub-band is partitioned into different blocks each of size 8 x 8. The pixel values of each block are quantized using a quantization table. Each quantized-block is further decomposed by the DCT, to generate the AC coefficient matrix. Scrambled watermark is thereafter inserted into the AC coefficient matrix using the perceptually tuned dynamic embedding strength factor. The visual quality and robustness of the proposed watermarking are balanced by optimizing the value of perceptually tuned strength factor by the Artificial bee colony (ABC) optimization algorithm. Experimental results show that the color watermark is undetectable (with CPSNR > 40 dB) into the host image and robust enough (with NC > 0.9) to resist the image processing and manipulation attacks. Comparison analysis with other related watermarking methods highlights the efficacy of the proposed work.

Compressed domain based robust digital video watermarking scheme to protect the copyright

<p><span>Digital video watermarking is an effective way to protect the ownership of the multimedia contents. A novel compressed domain based digital video watermarking algorithm scheme is proposed by exploiting MPEG-2 structure. Watermark bits are embedded in DC and AC coefficients both of only smooth discrete cosine transform (DCT) blocks from selected I-frames in the original digital video. The algorithms never exploited entire frames but explore only three location from the subset of DCT blocks from the subgroup of I-frames only. This process maintains the perceptibility of the watermarked video. Two parameters, normalized correlation (NC) and bit error rate (BER) are used to evaluate the degree of similarity and dissimilarity respectively to check the robustness against image processing and video specific intentional and non-intentional attacks. The security of embedded watermark is enhanced by applying three cryptographic keys. The experimental results demonstrated that the better robustness and perceptibility achieved by comparing the results with the state of art. </span></p>

An image retrieval scheme based on block level hybrid dct-svd fused features

In this paper, an image retrieval scheme has been proposed based on block level hybrid features. The block level salient feature are extracted in two parts: first level features are formed after the application of DCT and second level features are obtained after the processing of SVD. In the first level feature, salient components are computed from image blocks based on DCT transformation, which results into DC and AC coefficients. Here, the DC component is considered as the first level feature and the AC components are processed further to get the second level feature. Now, to extract second level feature, SVD is applied over the AC components which results into singular, left singular and right singular matrices. Based on the values of left and right singular matrices, some statistical parameters are computed which serve as the second level feature for the proposed scheme. To highlight the importance of extracted feature a weight factor is assigned to both first and second level features. However, more weight is given to the significant feature i.e the first level feature than the second level feature. Also, the feature extraction process is carried out separately for all the three planes of a color image, which in return gives more detailed feature for the proposed scheme. For the retrieval mechanism, similarity is measured by utilizing five existing distance measure schemes and the results are thoroughly analyzed to check the retrieval efficiency of the proposed scheme. Due to the variable weight factor, experimental results shows decent retrieval performance and the work is comparable to the existing works in image retrieval domain.

Content and Privacy Protection in JPEG Images by Reversible Visual Transformation

With the popularity of cloud computing and social networks, more and more JPEG images are stored and distributed. Consequently, how to protect privacy and content in JPEG images has become an important issue. Although traditional encryption schemes can be employed, the file format of JPEG images is changed so that their usage may be affected. In this paper, a reversible visual transformation algorithm is proposed to protect content in JPEG images. Specifically, the DC coefficient in each user-selected block is modified, while the information required to recover it is reversibly hidden into AC coefficients. Then the signs of AC coefficients in the selected blocks are flipped and the blocks are further scrambled with a secret key. By embedding the location information of the selected blocks in a transformed image, the original image can be exactly recovered when needed. Besides, regions to be protected can be arbitrarily chosen without substantially affecting the rest of the image. The experimental results on a set of JPEG images validate the efficacy and reversibility of the proposed algorithm. In addition, good performance is achieved in terms of invisibility of the protected content, image quality, file size preservation and security.