Showing papers in "Multimedia Tools and Applications in 2016"

Steganalysis of LSB matching using differences between nonadjacent pixels

Abstract: This paper models the messages embedded by spatial least significant bit (LSB) matching as independent noises to the cover image, and reveals that the histogram of the differences between pixel gray values is smoothed by the stego bits despite a large distance between the pixels Using the characteristic function of difference histogram (DHCF), we prove that the center of mass of DHCF (DHCF COM) decreases after messages are embedded Accordingly, the DHCF COMs are calculated as distinguishing features from the pixel pairs with different distances The features are calibrated with an image generated by average operation, and then used to train a support vector machine (SVM) classifier The experimental results prove that the features extracted from the differences between nonadjacent pixels can help to tackle LSB matching as well

An efficient and noise resistive selective image encryption scheme for gray images based on chaotic maps and DNA complementary rules

Abstract: A novel image encryption algorithm in streaming mode is proposed which exhaustively employs an entire set of DNA complementary rules alongwith one dimensional chaotic maps. The proposed algorithm is highly efficient due to encrypting the subset of digital image which contains 92.125 % of information. DNA addition operation is carried out on this MSB part. The core idea of the proposed scheme is to scramble the whole image by means of piecewise linear chaotic map (PWLCM) followed by decomposition of image into most significant bits (MSB) and least significant bits (LSB). The logistic sequence is XORed with the decoded MSB and LSB parts separately and finally these two parts are combined to get the ciphered image. The parameters for PWLCM, logistic map and selection of different DNA rules for encoding and decoding of both parts of an image are derived from 128-bit MD5 hash of the plain image. Simulated experimental results in terms of quantitative and qualitative ways prove the encryption quality. Efficiency and robustness against different noises make the proposed cipher a good candidate for real time applications.

Visual Instance Retrieval with Deep Convolutional Networks

Abstract: This paper provides an extensive study on the availability of image representations based on convolutional networks (ConvNets) for the task of visual instance retrieval. Besides the choice of convo ...

Hand gesture recognition with jointly calibrated Leap Motion and depth sensor

Abstract: Novel 3D acquisition devices like depth cameras and the Leap Motion have recently reached the market. Depth cameras allow to obtain a complete 3D description of the framed scene while the Leap Motion sensor is a device explicitly targeted for hand gesture recognition and provides only a limited set of relevant points. This paper shows how to jointly exploit the two types of sensors for accurate gesture recognition. An ad-hoc solution for the joint calibration of the two devices is firstly presented. Then a set of novel feature descriptors is introduced both for the Leap Motion and for depth data. Various schemes based on the distances of the hand samples from the centroid, on the curvature of the hand contour and on the convex hull of the hand shape are employed and the use of Leap Motion data to aid feature extraction is also considered. The proposed feature sets are fed to two different classifiers, one based on multi-class SVMs and one exploiting Random Forests. Different feature selection algorithms have also been tested in order to reduce the complexity of the approach. Experimental results show that a very high accuracy can be obtained from the proposed method. The current implementation is also able to run in real-time.

Automated classification of brain images using wavelet-energy and biogeography-based optimization

Abstract: It is very important to early detect abnormal brains, in order to save social and hospital resources. The wavelet-energy was a successful feature descriptor that achieved excellent performances in various applications; hence, we proposed a novel wavelet-energy based approach for automated classification of MR brain images as normal or abnormal. SVM was used as the classifier, and biogeography-based optimization (BBO) was introduced to optimize the weights of the SVM. The results based on a 5ź×ź5-fold cross validation showed the performance of the proposed BBO-KSVM was superior to BP-NN, KSVM, and PSO-KSVM in terms of sensitivity and accuracy. The study offered a new means to detect abnormal brains with excellent performance.

Chaos-based image encryption scheme combining DNA coding and entropy

Abstract: Information security has became more and more important issue in modern society, one of which is the digital image protection. In this paper, a secure image encryption scheme based on logistic and spatiotemporal chaotic systems is proposed. The extreme sensitivity of chaotic system can greatly increase the complexity of the proposed scheme. Further more, the scheme also takes advantage of DNA coding and eight DNA coding rules are mixed to enhance the efficiency of image confusion and diffusion. To resist the chosen-plaintext attack, information entropy of DNA coded image is modulated as the parameter of spatiotemporal chaotic system, which can also guarantee the sensitivity of plain image in the encryption process. So even a slight change in plain image can cause the complete change in cipher image. The experimental analysis shows that it can resistant different attacks, such as the brute-force attack, statistical attack and differential attack. What's more, The image encryption scheme can be easily implemented by software and is promising in practical application.

Hybrid technique for robust and imperceptible multiple watermarking using medical images

Abstract: This paper presents a secure multiple watermarking method based on discrete wavelet transform (DWT), discrete cosine transforms (DCT) and singular value decomposition (SVD). For identity authentication purpose, the proposed method uses medical image as the image watermark, and the personal and medical record of the patient as the text watermark. In the embedding process, the cover medical image is decomposed up to second level of DWT coefficients. Low frequency band (LL) of the host medical image is transformed by DCT and SVD. The watermark medical image is also transformed by DCT and SVD. The singular value of watermark image is embedded in the singular value of the host image. Furthermore, the text watermark is embedding at the second level of the high frequency band (HH) of the host image. In order to enhance the security of the text watermark, encryption is applied to the ASCII representation of the text watermark before embedding. Results are obtained by varying the gain factor, size of the text watermark, and medical image modalities. Experimental results are provided to illustrate that the proposed method is able to withstand a variety of signal processing attacks such as JPEG, Gaussian, Salt-and-Pepper, Histogram equalization etc. The performance of the proposed technique is also evaluated by using the benchmark software Checkmark and the technique is found to be robust against the Checkmark attacks such as Collage, Trimmed Mean, Hard and Soft Thresholding, Wavelet Compression, Mid Point, Projective, and Wrap etc.

A secure image encryption scheme based on chaotic maps and affine transformation

Abstract: Due to the interesting nonlinear dynamic properties of chaotic maps, recently chaos-based encryption algorithms have gained much attention in cryptographic communities. However, many encryption schemes do not fulfil the minimum key space requirement, which is an essential concern in many secure data applications. In this paper, an efficient chaos-based image encryption scheme with higher key space is presented. Even with a single round of encryption, a significantly larger key space can be achieved. The proposed scheme removes correlation among image pixels via random chaotic sequences, simply by XOR and addition operations. In order to resist against numerous attacks, we apply the affine transformation to get the final ciphertext image. The security of the proposed scheme is proved through histogram, contrast, PSNR, entropy, correlation, key space, key sensitivity and differential attack analysis. Many significant properties of chaotic maps, sensitivity to initial condition and control parameters, structure and attack complexity, make the anticipated scheme very reliable, practical and robust in various secure communication applications.

A novel magic LSB substitution method (M-LSB-SM) using multi-level encryption and achromatic component of an image

Abstract: Image Steganography is a thriving research area of information security where secret data is embedded in images to hide its existence while getting the minimum possible statistical detectability. This paper proposes a novel magic least significant bit substitution method (M-LSB-SM) for RGB images. The proposed method is based on the achromatic component (I-plane) of the hue-saturation-intensity (HSI) color model and multi-level encryption (MLE) in the spatial domain. The input image is transposed and converted into an HSI color space. The I-plane is divided into four sub-images of equal size, rotating each sub-image with a different angle using a secret key. The secret information is divided into four blocks, which are then encrypted using an MLE algorithm (MLEA). Each sub-block of the message is embedded into one of the rotated sub-images based on a specific pattern using magic LSB substitution. Experimental results validate that the proposed method not only enhances the visual quality of stego images but also provides good imperceptibility and multiple security levels as compared to several existing prominent methods.

A robust image encryption algorithm resistant to attacks using DNA and chaotic logistic maps

Abstract: An image encryption technique using DNA (Deoxyribonucleic acid) operations and chaotic maps has been proposed in this paper. Firstly, the input image is DNA encoded and a mask is generated by using 1D chaotic map. This mask is added with the DNA encoded image using DNA addition. Intermediate result is DNA complemented with the help of a complement matrix produced by two 1D chaotic maps. Finally, the resultant matrix is permuted using 2D chaotic map followed by DNA decoding to get the cipher image. Proposed technique is totally invertible and it can resist known plain text attack, statistical attacks and differential attacks.

Hash key-based image encryption using crossover operator and chaos

Abstract: This paper proposes a color image encryption scheme using one-time keys based on crossover operator, chaos and the Secure Hash Algorithm(SHA-2). The (SHA-2) is employed to generate a 256-bit hash value from both the plain-image and the secret hash keys to make the key stream change in each encryption process. The SHA-2 value is employed to generate three initial values of the chaotic system. The permutation-diffusion process is based on the crossover operator and XOR operator, respectively. Experimental results and security analysis show that the scheme can achieve good encryption result through only one round encryption process, the key space is large enough to resist against common attacks,so the scheme is reliable to be applied in image encryption and secure communication.

Human fall detection in surveillance video based on PCANet

Abstract: Fall incidents have been reported as the second most common cause of death, especially for elderly people. Human fall detection is necessary in smart home healthcare systems. Recently various fall detection approaches have been proposed., among which computer vision based approaches offer a promising and effective way. In this paper, we proposed a new framework for fall detection based on automatic feature learning methods. First, the extracted frames, including human from video sequences of different views, form the training set. Then, a PCANet model is trained by using all samples to predict the label of every frame. Because a fall behavior is contained in many continuous frames, the reliable fall detection should not only analyze one frame but also a video sequence. Based on the prediction result of the trained PCANet model for each frame, an action model is further obtained by SVM with the predicted labels of frames in video sequences. Experiments show that the proposed method achieved reliable results compared with other commonly used methods based on the multiple cameras fall dataset, and a better result is further achieved in our dataset which contains more training samples.

A new secure and sensitive image encryption scheme based on new substitution with chaotic function

Abstract: In this paper, a new image encryption scheme is proposed with high sensitivity to the plain image. In proposed scheme, two chaotic functions and logical operator xor are used. Image encryption process includes substitution of pixels and permutation. Using the new method of substitution, algorithm sensitivity somewhat has elevated to changes in the plain image that by changing a single pixel of the plain image, amount of NPCR reaches 100 %. Results of tests show that the cipher image does not give any information of statistical such as entropy, histogram and correlation of adjacent pixels to attackers. Also the proposed scheme has the wide key space and is so safe to the noise ratio and compression.

A new method for violence detection in surveillance scenes

Abstract: Violence detection is a hot topic for surveillance systems. However, it has not been studied as much as for action recognition. Existing vision-based methods mainly concentrate on violence detection and make little effort to determine the location of violence. In this paper, we propose a fast and robust framework for detecting and localizing violence in surveillance scenes. For this purpose, a Gaussian Model of Optical Flow (GMOF) is proposed to extract candidate violence regions, which are adaptively modeled as a deviation from the normal behavior of crowd observed in the scene. Violence detection is then performed on each video volume constructed by densely sampling the candidate violence regions. To distinguish violent events from nonviolent events, we also propose a novel descriptor, named as Orientation Histogram of Optical Flow (OHOF), which are fed into a linear SVM for classification. Experimental results on several benchmark datasets have demonstrated the superiority of our proposed method over the state-of-the-arts in terms of both detection accuracy and processing speed, even in crowded scenes.

A video steganography algorithm based on Kanade-Lucas-Tomasi tracking algorithm and error correcting codes

Abstract: Due to the significant growth of video data over the Internet, video steganography has become a popular choice. The effectiveness of any steganographic algorithm depends on the embedding efficiency, embedding payload, and robustness against attackers. The lack of the preprocessing stage, less security, and low quality of stego videos are the major issues of many existing steganographic methods. The preprocessing stage includes the procedure of manipulating both secret data and cover videos prior to the embedding stage. In this paper, we address these problems by proposing a novel video steganographic method based on Kanade-Lucas-Tomasi (KLT) tracking using Hamming codes (15, 11). The proposed method consists of four main stages: a) the secret message is preprocessed using Hamming codes (15, 11), producing an encoded message, b) face detection and tracking are performed on the cover videos, determining the region of interest (ROI), defined as facial regions, c) the encoded secret message is embedded using an adaptive LSB substitution method in the ROIs of video frames. In each facial pixel 1 LSB, 2 LSBs, 3 LSBs, and 4 LSBs are utilized to embed 3, 6, 9, and 12 bits of the secret message, respectively, and d) the process of extracting the secret message from the RGB color components of the facial regions of stego video is executed. Experimental results demonstrate that the proposed method achieves higher embedding capacity as well as better visual quality of stego videos. Furthermore, the two preprocessing steps increase the security and robustness of the proposed algorithm as compared to state-of-the-art methods.

Abnormal event detection in crowded scenes based on deep learning

Abstract: In this paper, we propose to use the deep learning technique for abnormal event detection by extracting spatiotemporal features from video sequences. Human eyes are often attracted to abnormal events in video sequences, thus we firstly extract saliency information (SI) of video frames as the feature representation in the spatial domain. Optical flow (OF) is estimated as an important feature of video sequences in the temporal domain. To extract the accurate motion information, multi-scale histogram optical flow (MHOF) can be obtained through OF. We combine MHOF and SI into the spatiotemporal features of video frames. Finally a deep learning network, PCANet, is adopted to extract high-level features for abnormal event detection. Experimental results show that the proposed abnormal event detection method can obtain much better performance than the existing ones on the public video database.

Copy-move forgery detection based on scaled ORB

Abstract: To solve the problem of the false matching and low robustness in detecting copy-move forgeries, a new method was proposed in this study. It involves the following steps: first, establish a Gaussian scale space; second, extract the orientated FAST key points and the ORB features in each scale space; thirdly, revert the coordinates of the orientated FAST key points to the original image and match the ORB features between every two different key points using the hamming distance; finally, remove the false matched key points using the RANSAC algorithm and then detect the resulting copy-move regions. The experimental results indicate that the new algorithm is effective for geometric transformation, such as scaling and rotation, and exhibits high robustness even when an image is distorted by Gaussian blur, Gaussian white noise and JPEG recompression; the new algorithm even has great detection on the type of hiding object forgery.

A review on automatic detection and recognition of traffic sign

Abstract: Evidently, Intelligent Transport System (ITS) has progressed tremendously all its way. The core of ITS are detection and recognition of traffic sign, which are designated to fulfill safety and comfort needs of driver. This paper provides a critical review on three major steps in Automatic Traffic Sign Detection and Recognition(ATSDR) system i.e., segmentation, detection and recognition in the context of vision based driver assistance system. In addition, it focuses on different experimental setups of image acquisition system. Further, discussion on possible future research challenges is made to make ATSDR more efficient, which inturn produce a wide range of opportunities for the researchers to carry out the detailed analysis of ATSDR and to incorporate the future aspects in their research.

A collaborative recommender system enhanced with particle swarm optimization technique

Abstract: In a web environment, one of the most evolving application is those with recommendation system (RS). It is a subset of information filtering systems wherein, information about certain products or services or a person are categorized and are recommended for the concerned individual. Most of the authors designed collaborative movie recommendation system by using K-NN and K-means but due to a huge increase in movies and users quantity, the neighbour selection is getting more problematic. We propose a hybrid model based on movie recommender system which utilizes type division method and classified the types of the movie according to users which results reduce computation complexity. K-Means provides initial parameters to particle swarm optimization (PSO) so as to improve its performance. PSO provides initial seed and optimizes fuzzy c-means (FCM), for soft clustering of data items (users), instead of strict clustering behaviour in K-Means. For proposed model, we first adopted type division method to reduce the dense multidimensional data space. We looked up for techniques, which could give better results than K-Means and found FCM as the solution. Genetic algorithm (GA) has the limitation of unguided mutation. Hence, we used PSO. In this article experiment performed on Movielens dataset illustrated that the proposed model may deliver high performance related to veracity, and deliver more predictable and personalized recommendations. When compared to already existing methods and having 0.78 mean absolute error (MAE), our result is 3.503 % better with 0.75 as the MAE, showed that our approach gives improved results.

An efficient and secure authentication and key agreement scheme for session initiation protocol using ECC

Abstract: The session initiation protocol (SIP) is a powerful and superior signaling protocol for the voice over internet protocol (VoIP) Authentication is an important security requirement for SIP Hitherto, many authentication schemes have been proposed to enhance the security of SIP Recently, Irshad et al proposed an improved authentication scheme concerning SIP, in which they claimed that their scheme is secure against various security attacks However, in this paper, we conclude that Irshad et al's scheme is vulnerable to user impersonation attacks Furthermore, a novel authentication and key agreement scheme is proposed for SIP using elliptic curve cryptosystem (ECC) Security and performance analyses demonstrate that the proposed scheme is secure against security attacks of various types and has low computation cost compared to previously proposed schemes

Landmark recognition with compact BoW histogram and ensemble ELM

Abstract: Along with the rapid development of mobile terminal devices, landmark recognition applications based on mobile devices have been widely researched in recent years. Due to the fast response time requirement of mobile users, an accurate and efficient landmark recognition system is thus urgent for mobile applications. In this paper, we propose a landmark recognition framework by employing a novel discriminative feature selection method and the improved extreme learning machine (ELM) algorithm. The scalable vocabulary tree (SVT) is first used to generate a set of preliminary codewords for landmark images. An efficient codebook learning algorithm derived from the word mutual information and Visual Rank technique is proposed to filter out those unimportant codewords. Then, the selected visual words, as the codebook for image encoding, are used to produce a compact Bag-of-Words (BoW) histogram. The fast ELM algorithm and the ensemble approach using the ELM classifier are utilized for landmark recognition. Experiments on the Nanyang Technological University campus's landmark database and the Fifteen Scene database are conducted to illustrate the advantages of the proposed framework.

Single image rain and snow removal via guided L0 smoothing filter

Abstract: Since no temporal information can be exploited, rain and snow removal from single image is a challenging problem. In this paper, an improved rain and snow removal method from single image is proposed by designing a guided L0 smoothing filter. The designed filter is inspired by the previous L0 gradient minimization. Then a coarse rain-free or snow-free image can be obtained with the proposed filter, and the final refined result is recovered by a further minimization operation depending on the observed image. Experimental results show that the proposed algorithm generates better or comparable outputs than the state-of-the-art algorithms in rain and snow removal task for single image.

A multimodal feature learning approach for sentiment analysis of social network multimedia

Abstract: In this paper we investigate the use of a multimodal feature learning approach, using neural network based models such as Skip-gram and Denoising Autoencoders, to address sentiment analysis of micro-blogging content, such as Twitter short messages, that are composed by a short text and, possibly, an image. The approach used in this work is motivated by the recent advances in: i) training language models based on neural networks that have proved to be extremely efficient when dealing with web-scale text corpora, and have shown very good performances when dealing with syntactic and semantic word similarities; ii) unsupervised learning, with neural networks, of robust visual features, that are recoverable from partial observations that may be due to occlusions or noisy and heavily modified images. We propose a novel architecture that incorporates these neural networks, testing it on several standard Twitter datasets, and showing that the approach is efficient and obtains good classification results.

Facial expression recognition based on a mlp neural network using constructive training algorithm

Abstract: This paper presents a constructive training algorithm for Multi Layer Perceptron (MLP) applied to facial expression recognition applications. The developed algorithm is composed by a single hidden-layer using a given number of neurons and a small number of training patterns. When the Mean Square Error MSE on the Training Data TD is not reduced to a predefined value, the number of hidden neurons grows during the neural network learning. Input patterns are trained incrementally until all patterns of TD are presented and learned. The proposed MLP constructive training algorithm seeks to find synthesis parameters as the number of patterns corresponding for subsets of each class to be presented initially in the training step, the initial number of hidden neurons, the number of iterations during the training step as well as the MSE predefined value. The suggested algorithm is developed in order to classify a facial expression. For the feature extraction stage, a biological vision-based facial description, namely Perceived Facial Images PFI has been applied to extract features from human face images. To evaluate, the proposed approach is tested on three databases which are the GEMEP FERA 2011, the Cohn-Kanade facial expression and the facial expression recognition FER-2013 databases. Compared to the fixed MLP architecture and the literature review, experimental results clearly demonstrate the efficiency of the proposed algorithm.

Adaptive pixel value differencing steganography using both vertical and horizontal edges

Abstract: This paper proposes two pixel value differencing (PVD) steganography techniques by considering adaptive ranges to improve the security. In the first technique, the image is partitioned into 2ź?ź2 pixel blocks in a non-overlapping fashion and scanned in raster-scan order. For every 2ź?ź2 pixel block the left-upper and bottom-right corner pixels are targetted based on their correlation with the other two pixels. Both horizontal and vertical edges are considered. In the second technique, the image is partitioned into blocks with 3ź?ź3 pixels in an overlapped fashion and scanned in raster-scan order. For a block the central pixel is targetted for embedding. Both the horizontal and vertical edges are inspected, but one of them is considered for data embedding at the target pixel. The ranges are adaptively calculated based upon the local statistics of the blocks. The first technique provides higher hiding capacity and the second technique provides higher peak signal-to-noise ratio value.

Perceptual image hashing using center-symmetric local binary patterns

Abstract: Perceptual image hashing finds increasing attention in several multimedia security applications such as image identification/authentication, tamper detection, and watermarking. Robust feature extraction is the main challenge in hashing schemes. Local binary pattern (LBP) is a new feature which is due to its simplicity, discriminative power, computational efficiency, and robustness to illumination changes has been used in various image applications. In this paper, we propose a robust image hashing scheme using center-symmetric local binary patterns (CSLBP). In the proposed image hashing, CSLBP features are extracted from each non-overlapping block within the original gray-scale image. For each block, the final hash code is obtained by inner product of its CSLBP feature vector and a pseudorandom weight vector. Furthermore, singular value decomposition (SVD) is combined with CSLBP to introduce a more robust hashing method called SVD-CSLBP. Performances of the proposed hashing schemes are evaluated with two groups of popular applications in perceptual image hashing schemes: image identification and image authentication. Experimental results show that the proposed methods are robust to a wide range of distortions and attacks such as additive noise, blurring, brightness changes and JPEG compression. Moreover, the proposed methods have this capability to localize the tampering area, which is not possible in all hashing schemes.

Towards automated visual surveillance using gait for identity recognition and tracking across multiple non-intersecting cameras

Abstract: Despite the fact that personal privacy has become a major concern, surveillance technology is now becoming ubiquitous in modern society. This is mainly due to the increasing number of crimes as well as the essential necessity to provide secure and safer environment. Recent research studies have confirmed now the possibility of recognizing people by the way they walk i.e. gait. The aim of this research study is to investigate the use of gait for people detection as well as identification across different cameras. We present a new approach for people tracking and identification between different non-intersecting un-calibrated stationary cameras based on gait analysis. A vision-based markerless extraction method is being deployed for the derivation of gait kinematics as well as anthropometric measurements in order to produce a gait signature. The novelty of our approach is motivated by the recent research in biometrics and forensic analysis using gait. The experimental results affirmed the robustness of our approach to successfully detect walking people as well as its potency to extract gait features for different camera viewpoints achieving an identity recognition rate of 73.6 % processed for 2270 video sequences. Furthermore, experimental results confirmed the potential of the proposed method for identity tracking in real surveillance systems to recognize walking individuals across different views with an average recognition rate of 92.5 % for cross-camera matching for two different non-overlapping views.

The effects of students' motivation, cognitive load and learning anxiety in gamification software engineering education: a structural equation modeling study

Abstract: Past research has proven the significant effects of game-based learning on learning motivation and academic performance, and described the key factors in game-based design. Nonetheless, research on the correlations among learning motivation, cognitive load, learning anxiety and academic performance in gamified learning environments has been minimal. This study, therefore, aims to develop a Gamification Software Engineering Education Learning System (GSEELS) and evaluate the effects of gamification, learning motivation, cognitive load and learning anxiety on academic performance. By applying Structural Equation Modeling (SEM) to the empirical research, the questionnaire contains: 1. a Gamification Learning Scale; 2. a Learning Motivation Scale; 3. a Cognitive Load Scale; 4. a Learning Anxiety Scale; and 5. an Academic Performance Scale. A total of 107 undergraduates in two classes participated in this study. The Structural Equation Modeling (SEM) analysis includes the path directions and relationship between descriptive statistics, measurement model, structural model evaluation and five variables. The research results support all nine hypotheses, and the research findings also show the effects of cognitive load on learning anxiety, with strong learning motivation resulting from a low learning anxiety. As a result, it is further proven in this study that a well-designed GSEELS would affect student learning motivation and academic performance. Finally, the relationship model between gamification learning, learning motivation, cognitive load, learning anxiety and academic performance is elucidated, and four suggestions are proffered for instructors of software engineering education courses and for further research, so as to assist instructors in the application of favorable gamification teaching strategies.

Using similarity analysis to detect frame duplication forgery in videos

Abstract: Duplication of selected frames from a video to another location in the same video is one of the most common methods of video forgery. However, few algorithms have been suggested for detecting this tampering operation. This paper proposes an effective similarity-analysis-based method for frame duplication detection that is implemented in two stages. In the first stage, the features of each frame are obtained via SVD (Singular Value Decomposition). Next, the Euclidean distance is calculated between features of each frame and the reference frame. After dividing the video sequence into overlapping sub-sequences, the similarities between the sub-sequences are calculated, and then our algorithm identifies those video sequences with high similarity as candidate duplications. In the second stage, the candidate duplications are confirmed through random block matching. The experimental results show that our algorithm provides detection accuracy that is higher than the previous algorithms, and it has an outstanding performance in terms of time efficiency.

Image encryption algorithm based on chaotic system and dynamic S-boxes composed of DNA sequences

Abstract: A new image encryption scheme based on dynamic S-boxes combined with chaotic system is proposed. Different from traditional diffusion methods based on DNA operations, dynamic S-boxes composed of DNA sequences are used to diffuse the pixel values of the image. Simulation results and security analysis show that the proposed algorithm has good performance and ability to resist common attacks.