In this paper we describe a novel technique for detecting salient regions in an image. The detector is a generalization to affine invariance of the method introduced by Kadir and Brady [10]. The detector deems a region salient if it exhibits unpredictability in both its attributes and its spatial scale.

An affine invariant salient region detector

In this paper, an algorithm for multiple watermarking based on discrete wavelet transforms (DWT), discrete cosine transform (DCT) and singular value decomposition (SVD) has been proposed for healthcare applications. For identity authentication purpose, the proposed method uses three watermarks in the form of medical Lump image watermark, the doctor signature/identification code and diagnostic information of the patient as the text watermarks. In order to improve the robustness performance of the image watermark, Back Propagation Neural Network (BPNN) is applied to the extracted image watermark to reduce the noise effects on the watermarked image. The security of the image watermark is also enhanced by using Arnold transform before embedding into the cover. Further, the symptom and signature text watermarks are also encoded by lossless arithmetic compression technique and Hamming error correction code respectively. The compressed and encoded text watermark is then embedded into the cover image. Experimental results are obtained by varying the gain factor, different sizes of text watermarks and the different cover image modalities. The results are provided to illustrate that the proposed method is able to withstand a different of signal processing attacks and has been found to be giving excellent performance for robustness, imperceptibility, capacity and security simultaneously. The robustness performance of the method is also compared with other reported techniques. Finally, the visual quality of the watermarked image is evaluated by the subjective method also. This shows that the visual quality of the watermarked images is acceptable for diagnosis at different gain factors. Therefore the proposed method may find potential application in prevention of patient identity theft in healthcare applications.

A proposed secure multiple watermarking technique based on DWT, DCT and SVD for application in medicine

The convolutional neural network (CNN) has been widely used in image recognition field due to its good performance. This paper proposes a facial expression recognition method based on the CNN model. Regarding the complexity of the hierarchic structure of the CNN model, the activation function is its core, because the nonlinear ability of the activation function really makes the deep neural network have authentic artificial intelligence. Among common activation functions, the ReLu function is one of the best of them, but it also has some shortcomings. Since the derivative of the ReLu function is always zero when the input value is negative, it is likely to appear as the phenomenon of neuronal necrosis. In order to solve the above problem, the influence of the activation function in the CNN model is studied in this paper. According to the design principle of the activation function in CNN model, a new piecewise activation function is proposed. Five common activation functions (i.e., sigmoid, tanh, ReLu, leaky ReLus and softplus–ReLu, plus the new activation function) have been analysed and compared in facial expression recognition tasks based on the Keras framework. The Experimental results on two public facial expression databases (i.e., JAFFE and FER2013) show that the convolutional neural network based on the improved activation function has a better performance than most-of-the-art activation functions.

The Influence of the Activation Function in a Convolution Neural Network Model of Facial Expression Recognition

In this paper, we propose a new chaos-based encryption scheme for medical images. It is based on a combination of chaos and DNA computing under the scenario of two encryption rounds, preceded by a key generation layer, and follows the permutation-substitution-diffusion structure. The SHA-256 hash function alongside the initial secret keys is employed to produce the secret keys of the chaotic systems. Each round of the proposed algorithm involves six steps, i.e., block-based permutation, pixel-based substitution, DNA encoding, bit-level substitution (i.e., DNA complementing), DNA decoding, and bit-level diffusion. A thorough search of the relevant literature yielded only this time the pixel-based substitution and the bit-level substitution are used in cascade for image encryption. The key-streams in the bit-level substitution are based on the logistic-Chebyshev map, while the sine-Chebyshev map allows producing the key-streams in the bit-level diffusion. The final encrypted image is obtained by repeating once the previous steps using new secret keys. Security analyses and computer simulations both confirm that the proposed scheme is robust enough against all kinds of attacks. Its low complexity indicates its high potential for real-time and secure image applications.

/pdf/novel-medical-image-encryption-scheme-based-on-chaos-and-dna-2uax2wuaol.pdf

Novel Medical Image Encryption Scheme Based on Chaos and DNA Encoding

Dual watermarking framework for privacy protection and content authentication of multimedia

Digital watermarking has been suggested as a way to achieve digital protection. The aim of digital watermarking is to insert the secret data into the image without significantly affecting the visual quality. This study presents a robust block-based image watermarking scheme based on the singular value decomposition (SVD) and human visual system in the discrete wavelet transform (DWT) domain. The proposed method is considered to be a block-based scheme that utilises the entropy and edge entropy as HVS characteristics for the selection of significant blocks to embed the watermark, which is a binary watermark logo. The blocks of the lowest entropy values and edge entropy values are selected as the best regions to insert the watermark. After the first level of DWT decomposition, the SVD is performed on the low-low sub-band to modify several elements in its U matrix according to predefined conditions. The experimental results of the proposed scheme showed high imperceptibility and high robustness against all image processing attacks and several geometrical attacks using examples of standard and real images. Furthermore, the proposed scheme outperformed several previous schemes in terms of imperceptibility and robustness. The security issue is improved by encrypting a portion of the important information using Advanced Standard Encryption a key size of 192-bits (AES-192).

/pdf/block-based-discrete-wavelet-transform-singular-value-3yrlwff3qo.pdf

Block-based discrete wavelet transform-singular value decomposition image watermarking scheme using human visual system characteristics

A new reliable optimized image watermarking scheme based on the integer wavelet transform and singular value decomposition for copyright protection

Despite the fact that the two texture descriptors, the completed modeling of Local Binary Pattern (CLBP) and the Completed Local Binary Count (CLBC), have achieved a remarkable accuracy for invariant rotation texture classification, they inherit some Local Binary Pattern (LBP) drawbacks. The LBP is sensitive to noise, and different patterns of LBP may be classified into the same class that reduces its discriminating property. Although, the Local Ternary Pattern (LTP) is proposed to be more robust to noise than LBP, however, the latter's weakness may appear with the LTP as well as with LBP. In this paper, a novel completed modeling of the Local Ternary Pattern (LTP) operator is proposed to overcome both LBP drawbacks, and an associated completed Local Ternary Pattern (CLTP) scheme is developed for rotation invariant texture classification. The experimental results using four different texture databases show that the proposed CLTP achieved an impressive classification accuracy as compared to the CLBP and CLBC descriptors.

/pdf/completed-local-ternary-pattern-for-rotation-invariant-49z0jbvpkp.pdf

Completed Local Ternary Pattern for Rotation Invariant Texture Classification

The accurate diagnosis of Alzheimer’s disease (AD) plays an important role in patient treatment, especially at the disease’s early stages, because risk awareness allows the patients to undergo preventive measures even before the occurrence of irreversible brain damage. Although many recent studies have used computers to diagnose AD, most machine detection methods are limited by congenital observations. AD can be diagnosed-but not predicted-at its early stages, as prediction is only applicable before the disease manifests itself. Deep Learning (DL) has become a common technique for the early diagnosis of AD. Here, we briefly review some of the important literature on AD and explore how DL can help researchers diagnose the disease at its early stages.

/pdf/alzheimer-s-diseases-detection-by-using-deep-learning-3khxozy4gg.pdf

Alzheimer’s Diseases Detection by Using Deep Learning Algorithms: A Mini-Review

Singular Value Decomposition (SVD) comprises many important mathematical properties that are useful in numerous applications. Newly developed SVD-based watermarking schemes can effectively maintain minor changes despite the large altered singular values S caused by the attacks. Due to the stability and the properties of S, most of the researchers prefer to embed into S. However, despite satisfying the stability and robustness criteria, SVD-based image watermarking can still encounter false positive problems (FPP). Avoiding FPPs is one of the popular research topics in the field of SVD-based image watermarking. Satisfying robustness and imperceptibility requirements, as well as preventing FPPs, in SVD-based image watermarking is crucial in applications such as copyright protection and authentication. In this paper, false positive problem is studied, analysed and presented in detail. Different schemes are studied and classified based on the probability of exposure to false positive problem. All types of SVD-based embedding algorithms that leads to false positive problem and the related potential attacks has been evaluated using the reliability test as well as all solutions to false positive problem are reviewed. To understand how the attacks can threaten the rightful ownership and how to avoid these attacks, the three potential attacks of false positive problem has been demonstrated using recent proposed watermarking schemes. The main perspective of this paper is to gather all the issues belong to the false positive problem with SVD-based schemes.

Taha H. Rassem

Papers

Block-based discrete wavelet transform-singular value decomposition image watermarking scheme using human visual system characteristics

A new reliable optimized image watermarking scheme based on the integer wavelet transform and singular value decomposition for copyright protection

Completed Local Ternary Pattern for Rotation Invariant Texture Classification

Alzheimer’s Diseases Detection by Using Deep Learning Algorithms: A Mini-Review

Security analyses of false positive problem for the SVD-based hybrid digital image watermarking techniques in the wavelet transform domain