Uncompressed Images Research Articles

A JPEG blocking artifact detector for image forensics

Detection of JPEG blocking artifacts in an image plays an important role in the forensic analysis. JPEG anti-forensic techniques try to remove these artifacts to disguise forensic detectors. This paper proposes a new technique for detecting JPEG blocking artifacts. Unlike in uncompressed images, when a JPEG compressed image is cropped, there is a change in the inter-block correlation of DCT coefficients due to the shifting of blocking artifacts. We propose to use this change in correlation to detect the blockiness in an image. Experiments conducted on a large set of images show that the proposed detector outperforms the existing detectors in detecting blocking artifacts in anti-forensically modified images.

Convolutional neural network-based classification system design with compressed wireless sensor network images.

With the introduction of various advanced deep learning algorithms, initiatives for image classification systems have transitioned over from traditional machine learning algorithms (e.g., SVM) to Convolutional Neural Networks (CNNs) using deep learning software tools. A prerequisite in applying CNN to real world applications is a system that collects meaningful and useful data. For such purposes, Wireless Image Sensor Networks (WISNs), that are capable of monitoring natural environment phenomena using tiny and low-power cameras on resource-limited embedded devices, can be considered as an effective means of data collection. However, with limited battery resources, sending high-resolution raw images to the backend server is a burdensome task that has direct impact on network lifetime. To address this problem, we propose an energy-efficient pre- and post- processing mechanism using image resizing and color quantization that can significantly reduce the amount of data transferred while maintaining the classification accuracy in the CNN at the backend server. We show that, if well designed, an image in its highly compressed form can be well-classified with a CNN model trained in advance using adequately compressed data. Our evaluation using a real image dataset shows that an embedded device can reduce the amount of transmitted data by ∼71% while maintaining a classification accuracy of ∼98%. Under the same conditions, this process naturally reduces energy consumption by ∼71% compared to a WISN that sends the original uncompressed images.

Securing digital documents using digital watermarking

The intellectual property of digital documents has been protected by using many methods of digital watermarking. Digital documents have been so much of advantages over print documents. Digital documents are less expensive and easy to store, transport, and searched compared to traditional print documents. But it has its owner limitation too. A simple image editor can be used to modify and make a forged document. Digital documents can be tampered easily. In order to utilize the whole benefits of digital document, these limitations have to overcome these limitations by embedding some text, logo sequence that identifies the owner of the document..
 In this research LSB technique has been used to embedding Digital watermarking , the proposed method consist of two major part: the embedding part and extraction part. The BMP picture type has been used in embedding process for accuracy and uncompressed image, and it is the best type in embedding process. This technique used to discover the genuine documentThe experiments show the proposed technique has 100% accuracy in authenticating the genuine document and Multimedia content security.

Two‐stream neural networks to detect manipulation of JPEG compressed images

With the rapid spread of image editing software, anyone can easily create, distribute, and forge images. Although techniques to detect image forgery have been widely studied, current techniques have significant limitations, such as specific file formats, manipulations, or compression qualities. Although deep learning techniques have been introduced to detect various manipulations, such as blurring, median filtering, and Gaussian noise, these techniques are only suitable to detect forgeries of uncompressed images, and are difficult to apply in practice because most images are compressed for distribution. Therefore, a two-stream neural network approach for image forensics that is robust to compression is proposed. The two-stream neural network is based on constrained convolutional neural network and Markov characteristics to consider compression. Experimental results show that the proposed method overcomes current technique limitations.

Reversible data hiding in absolute moment block truncation coding compressed images using adaptive multilevel histogram shifting technique

Due to advancement in communication technology, data are transmitted over the network which is either confidential or private. So, the information security is one of the most critical factors considered when secret data is transmitted between two parties. Another important issue is the bandwidth utilisation for data transmission. Image steganography is a widely used technique for data hiding. It is used in critical applications like military and medical areas. Most of the work is done in uncompressed images, which leads to high storage and large bandwidth required for transmission. Keeping these two factors in mind, this paper presents the multilevel histogram shifting technique in the compressed domain with the addition of adaptive block division scheme to improve the embedding capacity as well as reduce the utilisation of the bandwidth. In this method, absolute moment block truncation coding (AMBTC) compression technique has been used for compression because of its good compression ratio.

Source–channel coding-based watermarking for self-embedding of JPEG images

Watermarking-based self-embedding schemes designed to combat the kind of malicious tampering in which a portion of the image is replaced with the fake content are mostly developed for the uncompressed images; and thus, are vulnerable against image processing attacks such as noise addition and image compression. This drawback makes them impractical especially when applied to the popular JPEG domain. Based on modeling the tampering as a source–channel coding problem, a self-embedding scheme for digital images in JPEG domain is proposed in this paper. A compressed version of the original image protected against tampering with the help of proper channel coding approach forms the watermark of our proposed method. Source coding is applied to recover the lost content, while channel coding helps the watermarked image to tolerate tampering. Since the proposed method is designed based on the JPEG model, it can be efficiently integrated into the JPEG standard. Experimental results show that the proposed method offers robustness against attacks such as noise addition and recompression, which is the shortcoming of available existing uncompressed-domain image self-embedding schemes. Moreover, satisfactory performance of the proposed method is stated in comparison with the major existing JPEG domain self-embedding schemes in terms of better robustness and image quality.

Statistical Detection of JPEG Traces in Digital Images in Uncompressed Formats

Intrinsic statistical properties of natural uncompressed images are used in image forensics for detecting the traces of previous processing operations. In this paper, we propose novel forensic detectors of JPEG compression traces in images stored in uncompressed formats, based on a theoretical analysis of Benford-Fourier coefficients computed on the $8\times 8$ block-Discrete Cosine Transform (DCT) domain. In fact, the distribution of such coefficients is derived theoretically both under the hypotheses of no compression and previous compression with a certain quality factor, allowing for the computation of the respective likelihood functions. Then, two classification tests based on different statistics are proposed, both relying on a discriminative threshold that can be determined without the need of any training phase. The statistical analysis is based on the only assumptions of generalized Gaussian distribution of DCT coefficients and independence among DCT frequencies, thus resulting in robust detectors applying to any uncompressed image. In fact, experiments on different datasets show that the proposed models are suitable for the images of different sizes and source cameras, thus overcoming dataset-dependence issues that typically affect the state-of-art techniques.

An improved anti-forensics JPEG compression using Least Cuckoo Search algorithm

ABSTRACTThe lossy nature of the JPEG compression leaves traces which are utilized by the forensic agents to identify the local tampering in the image. In this paper, a tricky anti-forensic method has been proposed to remove the traces left by the JPEG compression in both the spatial domain and discrete cosine transform domain. A novel Least Cuckoo Search algorithm is devised in the proposed anti-forensic compression scheme. Moreover, a new fitness function called histogram deviation is formulated in the optimization algorithm. The experimentation of the proposed anti-forensic compression scheme is performed over uncompressed images from UCID database. The performance of the proposed method is evaluated, and it is compared with the existing methods using PSNR, MSE and classification accuracy as measures. The experimentation ensued with promising results, i.e. accuracy of 0.97, PSNR of 44.34 dB, and MSE of 0.1789 which prove the efficacy of the proposed method.

Abstract ID: 203 Breast model validation for Monte Carlo evaluation of normalized glandular dose coefficients in mammography

In Monte Carlo simulations for estimation of normalized glandular dose coefficients (i.e. the ratio between the mean glandular dose (MGD) and incident air kerma at the breast upper surface), the radiosensitive breast tissue is modelled as a homogeneous mixture of adipose and glandular tissue. However, such an assumption has been shown to lead to an overestimation in MGD by an average of 30% [1] , [2] . This work aimed at comparing the homogeneous breast models proposed by different authors and in different quality assurance protocols to patient specific breast phantoms. To obtain these phantoms, uncompressed breast images were acquired with a CT scanner dedicated to the breast and segmented to classify each voxel into 4 categories: air, skin tissue, adipose tissue and glandular tissue. Then the compression which breasts undergo during a mammography exams was simulated via software in order to obtain 3D compressed patient specific breast phantoms which present an heterogeneous glandular distribution similar to that of the irradiated breast. The homogeneous breast phantom was simulated either with a 4-mm skin thickness (as adopted in the USA protocol), or with a 5-mm adipose layer simulating the skin and the subcutaneous adipose layer (as adopted in the EU protocol) or with a skin thickness of 1.45 mm (i.e. the skin thickness estimated from 3D breast images). A pre-validated Monte Carlo code based on GEANT4 toolkit ver. 10.00 [3] was employed to calculate the dose differences between the standard models and the patient specific breast phantom. This study provided an insight into possible bias of the standard models for breast dosimetry and an indication of new breast model for future normalized glandular dose calculations.

Reversible data hiding in encrypted AMBTC images

Signal processing in the encrypted domain has attracted a lot of attention due to the requirement for content security and privacy protection. Reversible data hiding in encrypted images (RDH-EI) is also a hot topic. However, the majority of the published techniques are designed for uncompressed images rather than JPEG-, VQ- and BTC-compressed images. In this paper, for the first time, a RDH-EI method for AMBTC images is proposed. In the proposed method, the higher mean and lower mean of a triple in an AMBTC-compressed image are encrypted by using stream cipher at first. Then, additional data can be embedded into the redundant space by using prediction error histogram modification technique. Experimental results and analysis demonstrate that, with the marked cipher-image, legal receivers are able to extract embedded data exactly by using a data hiding key, decrypt it to recover an image very similar to the original one by using an image encryption key, or extract additional data and recover the original image error free with both keys. The proposed method is applicable to real-time transmission due to the simple implementation of the algorithm and low computational complexity.

Countering JPEG anti-forensics based on noise level estimation

Quantization artifact and blocking artifact are the two types of well-known fingerprints of JPEG compression. Most JPEG forensic techniques are focused on these fingerprints. However, recent research shows that these fingerprints can be intentionally concealed via anti-forensics, which in turn makes current JPEG forensic methods vulnerable. A typical JPEG anti-forensic method is adding anti-forensic dither to DCT transform coefficients and erasing blocking artifact to remove the trace of compression history. To deal with this challenge in JPEG forensics, in this paper, we propose a novel countering method based on the noise level estimation to identify the uncompressed images from those forged ones. The experimental results show that the proposed method achieves superior performance on several image databases with only one-dimensional feature. It is also worth emphasizing that the proposed threshold-based method has explicit physical meaning and is simple to be implemented in practice. Moreover, we analyze the strategies available to the investigator and the forger in the case of that they are aware of the existence of each other. Game theory is used to evaluate the ultimate performance when both sides adopt their Nash equilibrium strategies.

High-quality JPEG compression history detection for fake uncompressed images

Authenticity is one of the most important evaluation factors of images for photography competitions or journalism. Unusual compression history of an image often implies the illicit intent of its author. Our work aims at distinguishing real uncompressed images from fake uncompressed images that are saved in uncompressed formats but have been previously compressed. To detect the potential image JPEG compression, we analyze the JPEG compression artifacts based on the tetrolet covering, which corresponds to the local image geometrical structure. Since the compression can alter the structure information, the tetrolet covering indexes may be changed if a compression is performed on the test image. Such changes can provide valuable clues about the image compression history. To be specific, the test image is first compressed with different quality factors to generate a set of temporary images. Then, the test image is compared with each temporary image block-by-block to investigate whether the tetrolet covering index of each 4×4 block is different between them. The percentages of the changed tetrolet covering indexes corresponding to the quality factors (from low to high) are computed and used to form the p-curve, the local minimum of which may indicate the potential compression. Our experimental results demonstrate the advantage of our method to detect JPEG compressions of high quality, even the highest quality factors such as 98, 99, or 100 of the standard JPEG compression, from uncompressed-format images. At the same time, our detection algorithm can accurately identify the corresponding compression quality factor.

Extreme compression of fingerprint image databases using the model-based transform

In this paper, a model-based compression algorithm is proposed to compress the fingerprint image data. The proposed model is based on parallel lines with identical orientations, arbitrary widths, and similar gray-level values positioned on a rectangle with a constant gray-level value as the background. This algorithm is referred to as Parallel Stroked Multi Line (PSML). A compression algorithm is developed based on Adaptive Geometrical Wavelet which employs PSML to preserve the fingerprint structure and minutiae. The proposed PSML algorithm has significant advantages over Wedgelets Transform and JPEG2000 in terms of PSNR value, visual quality of the compressed images, and compression effect on Automatic Fingerprint Identification Systems (AFIS). On the U.are.U 400 database, the mean EER rate for uncompressed images is 4.54%, whereas at extreme compression rates, like 267:1, this value is 49.41% and 6.22% for JPEG2000 and PSML, respectively. The results show a significant improvement compared to the standard JPEG2000 algorithm.

An effective method to detect seam carving

Seam carving, also known as content-aware image resizing, is the most popular image resizing algorithm nowadays. Therefore, detecting seam carving has become an important topic in image forensics. In this paper, an advanced statistical model, consisting of local derivative pattern, Markov transition probabilities, and subtractive pixel adjacency model, is proposed to determine if an image has been seam carved or not. The performance of the proposed feature set can be further improved, and the feature set's dimensionality can be largely reduced by utilizing linear support vector machine (SVM) based recursive feature elimination. With the linear SVM classifier, the experimental works have demonstrated that the proposed approach can successfully detect seam carving. It outperforms the state-of-the-art in general; in particular at the low carving rate cases, such as 5%, 10% and 20%, the average detection accuracy has been boosted from 66%, 75% and 87% to 81%, 90% and 96%, respectively. On detecting seam carving in JPEG images and geometrical transformed uncompressed images, the proposed approach has also shown promising performance.

Image-format-independent tampered image detection based on overlapping concurrent directional patterns and neural networks

With the advancement of photo editing software, digital documents can easily be altered, which causes some legal issues. This paper proposes an image authentication method, which determines whether an image is authentic. Unlike many existing methods that only work with images in the JPEG format, the proposed method is image format independent, implying that it works with both noncompressed images and images in all compression formats. To improve the authentication accuracy, some strategies, such as overlapping image blocks only on concurrent directions, using a two-scale local binary pattern operator, and choosing the mean deviation instead of the standard deviation, are applied. A back-propagation neural network (BPNN) is used instead of support vector machines (SVMs) for classification to make online learning easier and achieve higher accuracy. In our experiments, we used the CASIA Database (CASIA TIDE v1.0) of compressed images and the Columbia University Digital Video Multimedia (DVMM) dataset of uncompressed images to evaluate our image authentication method. This benchmark dataset includes two types of image tampering, namely image splicing and copy–move forgery. Experiments were performed using both the SVM and BPNN classifiers with various parameters. We determined that the BPNN achieved a higher accuracy of up to 97.26 %.

Decoding information on the state of electrotechnical objects by using Reed–Solomon codes

This paper considers the problem of digital information processing by the example of encoding and decoding the control signal of a wireless working cycle correction technology for an autonomous electrical grid. Being subjected to random interferences or intentional attacks, the encoded signal may be decoded incorrectly, causing disruption (change) in the system operation. In this work, we simulate different types of interference. For subsequent software implementation, algorithms are proposed that simulate group and single errors, as well as errors that are combinations of these two types. The effects of errors of different types on the results of decoding the compressed and uncompressed image files are investigated. This paper also proposes some options for representation of graphic files encoded in the software implementation. The files are encoded using the Reed–Solomon codes and are decoded using the Guruswami–Sudan list-decoding algorithm. A comparison between the files obtained after decoding for different types of errors simulated in the process of encoding is presented.

Separation of Wheat Seeds from Junk in a Dynamic System Using Morphological Properties

Wheat is the main food source of the humankind. After its harvest, it goes through many procedures from its separation from chaff to its packaging. With the development in technology, many of these procedures are realized with automatic systems which saves the manufacturer the cost of labour, time and provides the customer with more quality food. One of the main concerns of quality food production is to provide a customer with the product in its purest form which means the product must be separated from all foreign matters. In this study, type-1252 durum wheat seeds have been separated from junk using the morphological properties of wheat seeds through the uncompressed video image taken with the camera Prosilica GT2000c. The main references for the quality measurement of wheat seeds are the shape and the dimensions of a wheat seed. Aiming for high quality wheat grain storage with no junk, this article has adopted various image processing techniques from image preprocessing to feature extraction. The image processing has been realized in a computer environment and the results show that the image processing is successful and the detection of wheat seeds from junk was accurate.

Reversible Data Hiding in JPEG Images

Among various digital image formats used in daily life, the Joint Photographic Experts Group (JPEG) is the most popular. Therefore, reversible data hiding (RDH) in JPEG images is important and useful for many applications such as archive management and image authentication. However, RDH in JPEG images is considerably more difficult than that in uncompressed images because there is less information redundancy in JPEG images than that in uncompressed images, and any modification in the compressed domain may introduce more distortion in the host image. Furthermore, along with the embedding capacity and fidelity (visual quality), which have to be considered for uncompressed images, the storage size of the marked JPEG file should be considered. In this paper, based on the philosophy behind the JPEG encoder and the statistical properties of discrete cosine transform (DCT) coefficients, we present some basic insights into how to select quantized DCT coefficients for RDH. Then, a new histogram shifting-based RDH scheme for JPEG images is proposed, in which the zero coefficients remain unchanged and only coefficients with values 1 and −1 are expanded to carry message bits. Moreover, a block selection strategy based on the number of zero coefficients in each $8\,\times \,8$ block is proposed, which can be utilized to adaptively choose DCT coefficients for data hiding. Experimental results demonstrate that by using the proposed method we can easily realize high embedding capacity and good visual quality. The storage size of the host JPEG file can also be well preserved.

Spatial Steganalysis Using Contrast of Residuals

This letter proposes a novel scheme for spatial steganalysis based on contrast of residuals (CoR). After selecting complex blocks from an uncompressed image by a fluctuation function, the residuals are calculated from the selected blocks and the whole image after applying diverse filters. The CoR is represented as an angle and the norm of residuals is considered as the corresponding weight of angle, which is used as the new steganalysis feature. In the proposed scheme, no quantization and truncation is required and the effective information of long-range dependencies among pixels is kept properly. Also, the dimensionality of feature is linear with the number of residuals. The accuracy of proposed scheme is evaluated on HUGO and WOW algorithms, and the experimental results show that the proposed CoR feature has superior performance at low embedding rate with lower dimensionality.

Tampering Localization in Double Compressed Images by Investigating Noise Quantization

Noise is uniformly distributed throughout an untampered image. Tampering operations destroy this uniformity and introduce inconsistency in the tampered region. Hence, noise discrepancy is often investigated in forensic analysis of uncompressed digital images. However, noise in compressed images has got very little attention from the forensic experts. The JPEG compression process itself introduces uniform quantization noise throughout an image, making this investigation difficult. In this paper, the authors have proposed a new noise compression discrepancy model, which blindly estimates this discrepancy in the compressed images. Considering the smaller tampered region, SVM classifier was trained using noise features of test sub-images and its nonaligned recompressed versions. Each of the test sub-images was further classified using this classifier. Experimental results show that in some cases, the proposed approach can achieve better performance compared with other JPEG artefact based techniques.