Medical JPEG image steganography based on preserving inter-block dependencies
TL;DR: Experimental results show that the proposed scheme can cluster the inter-block embedding changes and perform better than the state-of-the-art steganographic method.
About: This article is published in Computers & Electrical Engineering.The article was published on 2017-08-25. It has received 133 citations till now. The article focuses on the topics: Quantization (image processing) & Lossless JPEG.
Citations
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TL;DR: To guarantee the validity of EHRs encapsulated in blockchain, this paper presents an attribute-based signature scheme with multiple authorities, in which a patient endorses a message according to the attribute while disclosing no information other than the evidence that he has attested to it.
Abstract: Electronic Health Records (EHRs) are entirely controlled by hospitals instead of patients, which complicates seeking medical advices from different hospitals. Patients face a critical need to focus on the details of their own healthcare and restore management of their own medical data. The rapid development of blockchain technology promotes population healthcare, including medical records as well as patient-related data. This technology provides patients with comprehensive, immutable records, and access to EHRs free from service providers and treatment websites. In this paper, to guarantee the validity of EHRs encapsulated in blockchain, we present an attribute-based signature scheme with multiple authorities, in which a patient endorses a message according to the attribute while disclosing no information other than the evidence that he has attested to it. Furthermore, there are multiple authorities without a trusted single or central one to generate and distribute public/private keys of the patient, which avoids the escrow problem and conforms to the mode of distributed data storage in the blockchain. By sharing the secret pseudorandom function seeds among authorities, this protocol resists collusion attack out of $N$ from $N-1$ corrupted authorities. Under the assumption of the computational bilinear Diffie-Hellman, we also formally demonstrate that, in terms of the unforgeability and perfect privacy of the attribute-signer, this attribute-based signature scheme is secure in the random oracle model. The comparison shows the efficiency and properties between the proposed method and methods proposed in other studies.
373 citations
Cites methods from "Medical JPEG image steganography ba..."
...According to the guidelines of the Health Insurance Portability and Accountability Act (HIPPA) [8], the privacy of patients should be preserved in the process of sharing EHRs [9-13]....
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TL;DR: Convolutional neural network based on convolutional layer, rectified linear unit layer, pooling layer, fully connected layer, and softmax layer based on Convolutional Neural Network was chosen as the method to detect AUD.
Abstract: Alcohol use disorder (AUD) is an important brain disease. It alters the brain structure. Recently, scholars tend to use computer vision based techniques to detect AUD. We collected 235 subjects, 114 alcoholic and 121 non-alcoholic. Among the 235 image, 100 images were used as training set, and data augmentation method was used. The rest 135 images were used as test set. Further, we chose the latest powerful technique-convolutional neural network (CNN) based on convolutional layer, rectified linear unit layer, pooling layer, fully connected layer, and softmax layer. We also compared three different pooling techniques: max pooling, average pooling, and stochastic pooling. The results showed that our method achieved a sensitivity of 96.88%, a specificity of 97.18%, and an accuracy of 97.04%. Our method was better than three state-of-the-art approaches. Besides, stochastic pooling performed better than other max pooling and average pooling. We validated CNN with five convolution layers and two fully connected layers performed the best. The GPU yielded a 149× acceleration in training and a 166× acceleration in test, compared to CPU.
140 citations
TL;DR: This article has utilized multiple chaotic iterative maps in order to propose a novel image encryption technique and tested the anticipated scheme against different performances analysis and compared it with already existing results.
Abstract: The propagation of information over insecure communication system is one of the most important aspect of digitally advance era. The electronic information is travels in form of binary bits. The secrecy of these digital contents is one of the most important issue of existing world. In this article, we have utilized multiple chaotic iterative maps in order to propose a novel image encryption technique. The suggested encryption added confusion as well as diffusion in offered scheme which is one of the most fundamental aspect of encryption technique. We have tested our anticipated scheme against different performances analysis and compared it with already existing results. The designed scheme is capable of providing an excellent privacy to digital images.
122 citations
01 Jul 2019
TL;DR: Experimental results and analysis prove that this novel coverless steganographic approach without any modification for transmitting secret color image has strong resistance to steganalysis, but also has desirable security and high hiding capability.
Abstract: Most of the existing image steganographic approaches embed the secret information imperceptibly into a cover image by slightly modifying its content. However, the modification traces will cause some distortion in the stego-image, especially when embedding color image data that usually contain thousands of bits, which makes successful steganalysis possible. In this paper, we propose a novel coverless steganographic approach without any modification for transmitting secret color image. In our approach, instead of modifying a cover image to generate the stego-image, steganography is realized by using a set of proper partial duplicates of a given secret image as stego-images, which are retrieved from a natural image database. More specifically, after dividing each database image into a number of non-overlapping patches and indexing those images based on the features extracted from these patches, we search for the partial duplicates of the secret image in the database to obtain the stego-images, each of which shares one or several visually similar patches with the secret image. At the receiver end, by using the patches of the stego-images, our approach can approximately recover the secret image. Since the stego-images are natural ones without any modification traces, our approach can resist all of the existing steganalysis tools. Experimental results and analysis prove that our approach not only has strong resistance to steganalysis, but also has desirable security and high hiding capability.
112 citations
TL;DR: This model is to propose the enhancement and matching for latent fingerprints using Scale Invariant Feature Transformation (SIFT), and the matching result is obtained satisfactory compare than minutiae points.
Abstract: Latent fingerprint identification is such a difficult task to law enforcement agencies and border security in identifying suspects. It is a too complicate due to poor quality images with non-linear distortion and complex background noise. Hence, the image quality is required for matching those latent fingerprints. The current researchers have been working based on minutiae points for fingerprint matching because of their accuracy are acceptable. In an effort to extend technology for fingerprint matching, our model is to propose the enhancementand matching for latent fingerprints using Scale Invariant Feature Transformation (SIFT). It has involved in two phases (i) Latent fingerprint contrast enhancement using intuitionistic type-2 fuzzy set (ii) Extract the SIFTfeature points from the latent fingerprints. Then thematching algorithm is performedwith n- number of images and scoresare calculated by Euclidean distance. We tested our algorithm for matching, usinga public domain fingerprint database such as FVC-2004 and IIIT-latent fingerprint. The experimental consequences indicatethe matching result is obtained satisfactory compare than minutiae points.
103 citations
References
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TL;DR: This paper provides a state-of-the-art review and analysis of the different existing methods of steganography along with some common standards and guidelines drawn from the literature and some recommendations and advocates for the object-oriented embedding mechanism.
1,572 citations
25 Apr 2001
TL;DR: The newly developed algorithm F5 withstands visual and statistical attacks, yet it still offers a large steganographic capacity because it implements matrix encoding to improve the efficiency of embedding and reduces the number of necessary changes.
Abstract: Many steganographic systems are weak against visual and statistical attacks. Systems without these weaknesses offer only a relatively small capacity for steganographic messages. The newly developed algorithm F5 withstands visual and statistical attacks, yet it still offers a large steganographic capacity. F5 implements matrix encoding to improve the efficiency of embedding. Thus it reduces the number of necessary changes. F5 employs permutative straddling to uniformly spread out the changes over the whole steganogram.
1,136 citations
TL;DR: This paper proposes an alternative and well-known machine learning tool-ensemble classifiers implemented as random forests-and argues that they are ideally suited for steganalysis.
Abstract: Today, the most accurate steganalysis methods for digital media are built as supervised classifiers on feature vectors extracted from the media. The tool of choice for the machine learning seems to be the support vector machine (SVM). In this paper, we propose an alternative and well-known machine learning tool-ensemble classifiers implemented as random forests-and argue that they are ideally suited for steganalysis. Ensemble classifiers scale much more favorably w.r.t. the number of training examples and the feature dimensionality with performance comparable to the much more complex SVMs. The significantly lower training complexity opens up the possibility for the steganalyst to work with rich (high-dimensional) cover models and train on larger training sets-two key elements that appear necessary to reliably detect modern steganographic algorithms. Ensemble classification is portrayed here as a powerful developer tool that allows fast construction of steganography detectors with markedly improved detection accuracy across a wide range of embedding methods. The power of the proposed framework is demonstrated on three steganographic methods that hide messages in JPEG images.
967 citations
18 May 2011
TL;DR: This paper summarizes the first international challenge on steganalysis called BOSS (an acronym for Break The authors' Steganographic System), explaining the motivations behind the organization of the contest, its rules together with reasons for them, and the steganographic algorithm developed for the contest.
Abstract: This paper summarizes the first international challenge on steganalysis called BOSS (an acronym for Break Our Steganographic System). We explain the motivations behind the organization of the contest, its rules together with reasons for them, and the steganographic algorithm developed for the contest. Since the image databases created for the contest significantly influenced the development of the contest, they are described in a great detail. Paper also presents detailed analysis of results submitted to the challenge. One of the main difficulty the participants had to deal with was the discrepancy between training and testing source of images - the so-called cover-source mismatch, which forced the participants to design steganalyzers robust w.r.t. a specific source of images. We also point to other practical issues related to designing steganographic systems and give several suggestions for future contests in steganalysis.
902 citations
TL;DR: This paper proposes a complete practical methodology for minimizing additive distortion in steganography with general (nonbinary) embedding operation and reports extensive experimental results for a large set of relative payloads and for different distortion profiles, including the wet paper channel.
Abstract: This paper proposes a complete practical methodology for minimizing additive distortion in steganography with general (nonbinary) embedding operation. Let every possible value of every stego element be assigned a scalar expressing the distortion of an embedding change done by replacing the cover element by this value. The total distortion is assumed to be a sum of per-element distortions. Both the payload-limited sender (minimizing the total distortion while embedding a fixed payload) and the distortion-limited sender (maximizing the payload while introducing a fixed total distortion) are considered. Without any loss of performance, the nonbinary case is decomposed into several binary cases by replacing individual bits in cover elements. The binary case is approached using a novel syndrome-coding scheme based on dual convolutional codes equipped with the Viterbi algorithm. This fast and very versatile solution achieves state-of-the-art results in steganographic applications while having linear time and space complexity w.r.t. the number of cover elements. We report extensive experimental results for a large set of relative payloads and for different distortion profiles, including the wet paper channel. Practical merit of this approach is validated by constructing and testing adaptive embedding schemes for digital images in raster and transform domains. Most current coding schemes used in steganography (matrix embedding, wet paper codes, etc.) and many new ones can be implemented using this framework.
726 citations