Biblio

The global proliferation of affordable photographing devices and readily-available face image and video editing software has caused a remarkable rise in face manipulations, e.g., altering face skin color using FaceApp. Such synthetic manipulations are becoming a very perilous problem, as altered faces not only can fool human experts but also have detrimental consequences on automated face identification systems (AFIS). Thus, it is vital to formulate techniques to improve the robustness of AFIS against digital face manipulations. The most prominent countermeasure is face manipulation detection, which aims at discriminating genuine samples from manipulated ones. Over the years, analysis of microtextural features using local image descriptors has been successfully used in various applications owing to their flexibility, computational simplicity, and performances. Therefore, in this paper, we study the possibility of identifying manipulated faces via local feature descriptors. The comparative experimental investigation of ten local feature descriptors on a new and publicly available DeepfakeTIMIT database is reported.

Since the neural networks are utilized to extract information from an image, Gatys et al. found that they could separate the content and style of images and reconstruct them to another image which called Style Transfer. Moreover, there are many feed-forward neural networks have been suggested to speeding up the original method to make Style Transfer become practical application. However, this takes a price: these feed-forward networks are unchangeable because of their fixed parameters which mean we cannot transfer arbitrary styles but only single one in real-time. Some coordinated approaches have been offered to relieve this dilemma. Such as a style-swap layer and an adaptive normalization layer (AdaIN) and soon. Its worth mentioning that we observed that the AdaIN layer only aligns the means and variance of the content feature maps with those of the style feature maps. Our method is aimed at presenting an operational approach that enables arbitrary style transfer in real-time, reserving more statistical information by histogram matching, providing more reliable texture clarity and more humane user control. We achieve performance more cheerful than existing approaches without adding calculation, complexity. And the speed comparable to the fastest Style Transfer method. Our method provides more flexible user control and trustworthy quality and stability.

In painting, humans can draw an interrelation between the style and the content of a given image in order to enhance visual experiences. Deep neural networks like convolutional neural networks are being used to draw a satisfying conclusion of this problem of neural style transfer due to their exceptional results in the key areas of visual perceptions such as object detection and face recognition.In this study, along with style transfer on whole image it is also outlined how transfer of style can be performed only on the specific parts of the content image which is accomplished by using masks. The style is transferred in a way that there is a least amount of loss to the content image i.e., semantics of the image is preserved.

Most of the malware detection techniques use malware signatures for detection. It is easy to detect known malicious program in a system but the problem arises when the malware is unknown. Because, unknown malware cannot be detected by using available known malware signatures. Signature based detection techniques fails to detect unknown and zero-day attacks. A novel approach is required to represent malware features effectively to detect obfuscated, unknown, and mutated malware. This paper emphasizes malware behavior, characteristics and properties extracted by different analytic techniques and to decide whether to include them to create behavioral based malware signature. We have made an attempt to understand the malware behavior using a few openly available tools for malware analysis.

This work presents a new method of encrypting and decrypting speech based on a chaotic key generator. The proposed scheme takes advantage of the best features of chaotic systems. In the proposed method, the input speech signal is converted into an image which is ciphered by an encryption function using a chaotic key matrix generated from a fractional-order chaotic map. Based on a deadbeat observer, the exact synchronization of system used is established, and the decryption is performed. Different analysis are applied for analyzing the effectiveness of the encryption system. The obtained results confirm that the proposed system offers a higher level of security against various attacks and holds a strong key generation mechanism for satisfactory speech communication.

The purpose of this paper is to improve the safety of chaotic image encryption algorithm. Firstly, to achieve this goal, it put forward two improved chaotic system logistic and henon, which covered an promoted henon chaotic system with better probability density, and an 2-dimension logistic chaotic system with high Lyapunov exponents. Secondly, the chaotic key stream was generated by the new 2D logistic chaotic system and optimized henon mapping, which mixed in dynamic proportions. The conducted sequence has better randomness and higher safety for image cryptosystem. Thirdly, we proposed algorithm takes advantage of the compounded chaotic system Simulation experiment results and security analysis showed that the proposed scheme was more effective and secure. It can resist various typical attacks, has high security, satisfies the requirements of image encryption theoretical.

With the rapid development of the Internet, the dark network has also been widely used in the Internet [1]. Due to the anonymity of the dark network, many illegal elements have committed illegal crimes on the dark. It is difficult for law enforcement officials to track the identity of these cyber criminals using traditional network survey techniques based on IP addresses [2]. The threat information is mainly from the dark web forum and the dark web market. In this paper, we introduce the current mainstream dark network communication system TOR and develop a visual dark web forum post association analysis system to graphically display the relationship between various forum messages and posters, and help law enforcement officers to explore deep levels. Clues to analyze crimes in the dark network.

Nowadays, secure transmission of multimedia files has become more significant concern with the evolution of technologies. Cryptography is the well-known technique to safeguard the files from various destructive hacks. In this work, a colour image encryption scheme is suggested using chaos and Deoxyribo Nucleic Acid (DNA) coding. The encryption scheme is carried out in two stages namely confusion and diffusion. As the first stage, chaos aided inter-planar row and column shuffling are performed to shuffle the image pixels completely. DNA coding and decoding operations then diffuse the resultant confused image with the help of eight DNA XOR rules. This confusion-diffusion process has achieved the entropy value equal to 7.9973 and correlation coefficient nearer to zero with key space of 10140. Various other analyses are also done to ensure the effectiveness of the developed algorithm. The results show that the proposed scheme can withstand different attacks and better than the recent state-of-art methods.

In this paper, an image encryption scheme based on dynamic DNA sequence and two dimension logistic map is proposed. Firstly two different pseudo random sequences are generated using two dimension Sine-Henon alteration map. These sequences are used for altering the positions of each pixel of plain image row wise and column wise respectively. Secondly each pixels of distorted image and values of random sequences are converted into a DNA sequence dynamically using one dimension logistic map. Reversible DNA operations are applied between DNA converted pixel and random values. At last after decoding the results of DNA operations cipher image is obtained. Different theoretical analyses and experimental results proved the effectiveness of this algorithm. Large key space proved that it is possible to protect different types of attacks using our proposed encryption scheme.

The objective of this paper was to propose a 5D combined chaotic system used for image encryption by scrambling and DNA encryption. The initial chaotic values were calculated with a set of equations. The chaotic sequences were used for pixel scrambling, bit scrambling, DNA encryption and DNA complementary function. The average of NPCR, UACI and entropy values of the 6 images used for testing were 99.61, 33.51 and 7.997 respectively. The correlation values obtained for the encrypted image were much lower than the corresponding original image. The histogram of the encrypted image was flat. Based on the theoretical results from the tests performed on the proposed system it can be concluded that the system is suited for practical applications, since it offers high security.

Biometry is often used as a part of the multi-factor authentication in order to improve the security of IT systems. In this paper, we propose the palmprint-based solution for user identity verification. In particular, we present a new approach to feature extraction. The proposed method is based both on texture and color information. Our experiments show that using the proposed hybrid features allows for achieving satisfactory accuracy without increasing requirements for additional computational resources. It is important from our perspective since the proposed method is dedicated to smartphones and other handhelds in mobile verification scenarios.

Big data processing systems are becoming increasingly more present in cloud workloads. Consequently, they are starting to incorporate more sophisticated mechanisms from traditional database and distributed systems. We focus in this work on the use of caching policies, which for big data raise important new challenges. Not only they must respond to new variants of the trade-off between hit rate, response time, and the space consumed by the cache, but they must do so at possibly higher volume and velocity than web and database workloads. Previous caching policies have not been tested experimentally with big data workloads. We address these challenges in this work. We propose the Read Density family of policies, which is a principled approach to quantify the utility of cached objects through a family of utility functions that depend on the frequency of reads of an object. We further design the Approximate Histogram, which is a policy-based technique based on an array of counters. This technique promises to achieve runtime-space efficient computation of the metric required by the cache policy. We evaluate through trace-based simulation the caching policies from the Read Density family, and compare them with over ten state-of-the-art alternatives. We use two workload traces representative for big data processing, collected from commercial Spark and MapReduce deployments. While we achieve comparable performance to the state-of-art with less parameters, meaningful performance improvement for big data workloads remain elusive.

In this paper, a general content adaptive image steganography detector in the spatial domain is proposed. We assemble conventional Haar and LBP features to construct local co-occurrence features, then the boosted classifiers are used to assemble the features as well as the final detector, and each weak classifier of the boosted classifiers corresponds to the co-occurrence feature of a local image region. Moreover, the classification ability and the generalization power of the candidate features are both evaluated for decision in the feature selection procedure of boosting training, which makes the final detector more accuracy. The experimental results on standard dataset show that the proposed framework can detect two primary content adaptive stego algorithms in the spatial domain with higher accuracy than the state-of-the-art steganalysis method.

The natural redundancy in video data due to its spatio-temporal correlation of neighbouring pixels require highly complex encryption process to successfully cipher the data. Conventional encryption methods are based on lengthy keys and higher number of rounds which are inefficient for low powered, small battery operated devices. Motivated by the success of lightweight encryption methods specially designed for IoT environment, herein an efficient method for video encryption is proposed. The proposed technique is based on a recently proposed encryption algorithm named Secure IoT (SIT), which utilizes P and Q functions of the KHAZAD cipher to achieve high encryption at low computation cost. Extensive simulations are performed to evaluate the efficacy of the proposed method and results are compared with Secure Force (SF-64) cipher. Under all conditions the proposed method achieved significantly improved results.

Information security is winding up noticeably more vital in information stockpiling and transmission. Images are generally utilised for various purposes. As a result, the protection of image from the unauthorised client is critical. Established encryption techniques are not ready to give a secure framework. To defeat this, image encryption is finished through DNA encoding which is additionally included with confused 1D and 2D logistic maps. The key communication is done through the quantum channel using the BB84 protocol. To recover the encrypted image DNA decoding is performed. Since DNA encryption is invertible, decoding can be effectively done through DNA subtraction. It decreases the complexity and furthermore gives more strength when contrasted with traditional encryption plans. The enhanced strength of the framework is measured utilising measurements like NPCR, UACI, Correlation and Entropy.

In the literature, many chaotic systems have been used in the design of image encryption algorithms. In this study, an application of fractional order chaotic systems is investigated. The aim of the study is to improve the disadvantageous aspects of existing methods based on discrete and continuous time chaotic systems by utilizing the features of fractional order chaotic systems. The most important advantage of the study compared to the literature is that the proposed encryption algorithm is designed with a provable security approach. Analyses results have been shown that the proposed method can be used successfully in many information security applications.

In recent years, the big-data approach has become important within various business operations and sales judgment tactics. Contrarily, numerous privacy problems limit the progress of their analysis technologies. To mitigate such problems, this paper proposes several privacy-preserving methods, i.e., anonymization, extreme value record elimination, fully encrypted analysis, and so on. However, privacy-cracking fears still remain that prevent the open use of big-data by other, external organizations. We propose a big-data reconstruction method that does not intrinsically use privacy data. The method uses only the statistical features of big-data, i.e., its attribute histograms and their correlation coefficients. To verify whether valuable information can be extracted using this method, we evaluate the data by using Self Organizing Map (SOM) as one of the big-data analysis tools. The results show that the same pieces of information are extracted from our data and the big-data.

Gatys et al. recently introduced a neural algorithm that renders a content image in the style of another image, achieving so-called style transfer. However, their framework requires a slow iterative optimization process, which limits its practical application. Fast approximations with feed-forward neural networks have been proposed to speed up neural style transfer. Unfortunately, the speed improvement comes at a cost: the network is usually tied to a fixed set of styles and cannot adapt to arbitrary new styles. In this paper, we present a simple yet effective approach that for the first time enables arbitrary style transfer in real-time. At the heart of our method is a novel adaptive instance normalization (AdaIN) layer that aligns the mean and variance of the content features with those of the style features. Our method achieves speed comparable to the fastest existing approach, without the restriction to a pre-defined set of styles. In addition, our approach allows flexible user controls such as content-style trade-off, style interpolation, color & spatial controls, all using a single feed-forward neural network.

We regularly use communication apps like Facebook and WhatsApp on our smartphones, and the exchange of media, particularly images, has grown at an exponential rate. There are over 3 billion images shared every day on Whatsapp alone. In such a scenario, the management of images on a mobile device has become highly inefficient, and this leads to problems like low storage, manual deletion of images, disorganization etc. In this paper, we present a solution to tackle these issues by automatically classifying every image on a smartphone into a set of predefined categories, thereby segregating spam images from them, allowing the user to delete them seamlessly.

We present a gradient-based attack against SVM-based forensic techniques relying on high-dimensional SPAM features. As opposed to prior work, the attack works directly in the pixel domain even if the relationship between pixel values and SPAM features can not be inverted. The proposed method relies on the estimation of the gradient of the SVM output with respect to pixel values, however it departs from gradient descent methodology due to the necessity of preserving the integer nature of pixels and to reduce the effect of the attack on image quality. A fast algorithm to estimate the gradient is also introduced to reduce the complexity of the attack. We tested the proposed attack against SVM detection of histogram stretching, adaptive histogram equalization and median filtering. In all cases the attack succeeded in inducing a decision error with a very limited distortion, the PSNR between the original and the attacked images ranging from 50 to 70 dBs. The attack is also effective in the case of attacks with Limited Knowledge (LK) when the SVM used by the attacker is trained on a different dataset with respect to that used by the analyst.

In this paper, we introduce a fast, secure and robust scheme for digital image encryption using chaotic system of Lorenz, 4D hyper-chaotic system and the Secure Hash Algorithm SHA-1. The encryption process consists of three layers: sub-vectors confusion and two-diffusion process. In the first layer we divide the plainimage into sub-vectors then, the position of each one is changed using the chaotic index sequence generated with chaotic attractor of Lorenz, while the diffusion layers use hyper-chaotic system to modify the values of pixels using an XOR operation. The results of security analysis such as statistical tests, differential attacks, key space, key sensitivity, entropy information and the running time are illustrated and compared to recent encryption schemes where the highest security level and speed are improved.

In this paper, a novel quantum encryption algorithm for color image is proposed based on multiple discrete chaotic systems. The proposed quantum image encryption algorithm utilize the quantum controlled-NOT image generated by chaotic logistic map, asymmetric tent map and logistic Chebyshev map to control the XOR operation in the encryption process. Experiment results and analysis show that the proposed algorithm has high efficiency and security against differential and statistical attacks.

This paper address the problem of shadow detection and removal in traffic vision analysis. Basically, the presence of the shadow in the traffic sequences is imminent, and therefore leads to errors at segmentation stage and often misclassified as an object region or as a moving object. This paper presents a shadow removal method, based on both color and texture features, aiming to contribute to retrieve efficiently the moving objects whose detection are usually under the influence of cast-shadows. Additionally, in order to get a shadow-free foreground segmentation image, a morphology reconstruction algorithm is used to recover the foreground disturbed by shadow removal. Once shadows are detected, an automatic shadow removal model is proposed based on the information retrieved from the histogram shape. Experimental results on a real traffic sequence is presented to test the proposed approach and to validate the algorithm's performance.

The goal of this paper is to determine whether keystroke sound can be used to recognize a user. In this regard, we analyze the discriminative power of keystroke sound in the context of a continuous user authentication application. Motivated by the concept of digraphs used in modeling keystroke dynamics, a virtual alphabet is first learned from keystroke sound segments. Next, the digraph latency within the pairs of virtual letters, along with other statistical features, is used to generate match scores. The resultant scores are indicative of the similarities between two sound streams, and are fused to make a final authentication decision. Experiments on both static text-based and free text-based authentications on a database of 50 subjects demonstrate the potential as well as the limitations of keystroke sound.

Nowadays, many computer vision techniques are applied to practical applications, such as surveillance and facial recognition systems. Some of such applications focus on information extraction from the human beings. However, people may feel psychological stress about recording their personal information, such as a face, behavior, and cloth. Therefore, privacy protection of the images and videos is necessary. Specifically, the detection and tracking methods should be used on the privacy protected images. For this purpose, there are some easy methods, such as blurring and pixelating, and they are often used in news programs etc. Because such methods just average pixel values, no important feature for the detection and tracking is left. Hence, the preprocessed images are unuseful. In order to solve this problem, we have proposed shuffle filter and a multi-view face tracking method with a genetic algorithm (GA). The filter protects the privacy by changing pixel locations, and the color information can be preserved. Since the color information is left, the tracking can be achieved by a basic template matching with histogram. Moreover, by using GA instead of sliding window when the subject in the image is searched, it can search more efficiently. However, the tracking accuracy is still low and the preprocessing time is large. Therefore, improving them is the purpose in this research. In the experiment, the improved method is compared with our previous work, CAMSHIFT, an online learning method, and a face detector. The results indicate that the accuracy of the proposed method is higher than the others.