Copy move and splicing forgery detection using deep convolution neural network,and semantic segmentation

Multimedia Tools and Applications - Image forgeries can be detected and localized by using deep convolution neural network, and semantic segmentation. Color illumination is used to apply color map...     

Copy move forgery detection based on keypoint and patch match

Multimedia Tools and Applications - Copy move has become a simple and effective operation for image forgeries due to the advancement of image editing software, which is still challenging to be...     

Ciratefi based copy move forgery detection on digital images

Multimedia Tools and Applications - There is an increase in the requirement of digital image authentication in law, journalism, and medicine, even in the industry. Copy move forgery is the most...     

基于深度特征提取和DCT变换的图像复制粘贴篡改检测

针对图像复制粘贴篡改检测中算法时间复杂度过高和定位区域不完整的问题,提出一种基于深度特征提取和离散余弦变换的图像复制粘贴篡改检测算法。首先,融合图像颜色和纹理信息获得四通道图像,计算自适应特征提取阈值,并通过基于全卷积神经网络的特征检测器提取图像深度特征;其次,通过离散余弦变换提取块特征进行初步匹配,再利用点特征向量消除误匹配;最后,通过卷积运算精确定位篡改区域。通过在公共数据集上进行验证,充分展示了该算法在检测效率和定位区域完整性方面的优势。     

Image forgery detection using image similarity

Multimedia Tools and Applications - Ideally, sophisticated image forgery methods leave no perceptible evidence of tampering. In response to such stringent context, researchers have proposed digital...     

Neighborhood detection using mutual information for the identification of cellular automata.

Extracting the rules from spatio-temporal patterns generated by the evolution of cellular automata (CA) usually requires a priori information about the observed system, but in many applications little information will be known about the pattern. This paper introduces a new neighborhood detection algorithm which can determine the range of the neighborhood without any knowledge of the system by introducing a criterion based on mutual information (and an indication of over-estimation). A coarse-to-fine identification routine is then proposed to determine the CA rule from the observed pattern. Examples, including data from a real experiment, are employed to evaluate the new algorithm.     

A new block-based method for copy move forgery detection under image geometric transforms

Copy move forgery detection (CMFD) is one of the most active subtopic in forgery scheme. The methods of CMFD are divided into to block-based method and keypoint-based method in general. Compared with keypoint-based method, block-based method can detect undetectable detail without morphology segmentation. But many block-based methods detect the plain copy-move forgeries only. They have been incompetent to detect the post-processing operations such as various geometrical distortions, and then fail to detect the forgery regions accurately. Therefore, this paper presents an improved block-based efficient method for CMFD. Firstly, after pre-processing, an auxiliary overlapped circular block is presented to divide the forged image into overlapped circular blocks. The local and inner image feature is extracted by the Discrete Radial Harmonic Fourier Moments (DRHFMs) with the overlapped circular block from the suspicious image. Then, the similar feature vectors of blocks are searched by 2 Nearest Neighbors (2NN) test. Euclidean distance and correlation coefficient is employed to filter these features and then remove the false matches. Morphologic operation is employed to delete the isolated pixels. A series of experiments are done to analyze the performance for CMFD. Experimental results show that the new DRHFMs can obtain outstanding performance even under image geometrical distortions.     

Pyroclastic flows modelling using cellular automata

Cellular automata (CA) and derived computational paradigms represent an alternative approach to differential equations to model and simulating complex fluid dynamical systems, whose evolution depends on the local interactions of their constituent parts. A new notion of CA was developed according to an empirical method for modelling macroscopic phenomena; its application to PYR, a CA model for simulating pyroclastic flows, generated PYR2, which permitted an improvement of the model and a more efficient implementation. PYR2 was utilised for the 1991 eruption of Mt. Pinatubo in the Philippines islands and for the 1996 eruption of the Soufriere Hills in the Montserrat Island. Results of the simulations are satisfactory if the comparison between real and simulated event is performed, considering the area involved by the event and the variations of thickness of the deposit, as generated by collapsing volcanic columns.     

Text compression using two-dimensional cellular automata

This paper presents an elegant mathematical model using simple matrix algebra for characterising the behaviour of two-dimensional nearest neighbourhood linear cellular automata with periodic boundary conditions. Based on this mathematical model, the VLSI architecture of a Cellular Automata Machine (CAM) has been proposed for text compression. Experimental results of comparisons with adaptive Huffman coding scheme also presented.     

Retina simulation using cellular automata and GPU programming

This article shows how the architectural modelization of biological retina allows real-time performances, on standard widespread computing systems. First, we describe the biological retina with regard to its pipeline architecture, detailing its layer behaviours and properties. Then we propose a corresponding pipelined model of artificial retina based on cellular automata. In this work, the main innovation is the computing method based on the programming of a personal computer graphical card using OpenGL shading language. The last section demonstrates the efficiency of our model through numerical and graphical results. We lay particular emphasis on the fact that our direct implementation of the Graphical Processor Unit (GPU) provides computation power about 20 times as fast as conventional programming.     

Copy-move forgery detection using combined features and transitive matching

Recently, the research of Internet of Things (IoT) and Multimedia Big Data (MBD) has been growing tremendously. Both IoT and MBD have a lot of multimedia data, which can be tampered easily. Therefore, the research of multimedia forensics is necessary. Copy-move is an important branch of multimedia forensics. In this paper, a novel copy-move forgery detection scheme using combined features and transitive matching is proposed. First, SIFT and LIOP are extracted as combined features from the input image. Second, transitive matching is used to improve the matching relationship. Third, a filtering approach using image segmentation is proposed to filter out false matches. Fourth, affine transformations are estimated between these image patches. Finally, duplicated regions are located based on those affine transformations. The experimental results demonstrate that the proposed scheme can achieve much better detection results on the public database under various attacks.

     

Off-line signature verification and forgery detection using fuzzy modeling

Automatic signature verification is a well-established and an active area of research with numerous applications such as bank check verification, ATM access, etc. This paper proposes a novel approach to the problem of automatic off-line signature verification and forgery detection. The proposed approach is based on fuzzy modeling that employs the Takagi-Sugeno (TS) model. Signature verification and forgery detection are carried out using angle features extracted from box approach. Each feature corresponds to a fuzzy set. The features are fuzzified by an exponential membership function involved in the TS model, which is modified to include structural parameters. The structural parameters are devised to take account of possible variations due to handwriting styles and to reflect moods. The membership functions constitute weights in the TS model. The optimization of the output of the TS model with respect to the structural parameters yields the solution for the parameters. We have also derived two TS models by considering a rule for each input feature in the first formulation (Multiple rules) and by considering a single rule for all input features in the second formulation. In this work, we have found that TS model with multiple rules is better than TS model with single rule for detecting three types of forgeries; random, skilled and unskilled from a large database of sample signatures in addition to verifying genuine signatures. We have also devised three approaches, viz., an innovative approach and two intuitive approaches using the TS model with multiple rules for improved performance.     

Linear cellular automata and Fischer automata

We study the sizes of minimal finite state machines associated with linear cellular automata. In particular, we construct a class of binary linear cellular automata whose corresponding minimal automata exhibit full exponential blow-up. These cellular automata have Hamming distance 1 to a permutation automaton. Moreover, the corresponding minimal Fischer automata as well as the minimal DFAs have maximal complexity. By contrast, the complexity of higher iterates of a cellular automaton always stays below the theoretical upper bound.     

Data compression and encryption using cellular automata transforms

A family of basis functions, generated from the evolving states of Cellular Automata (CA), is used to compress and encrypt data. The operations required in encoding and decoding the data are described under the umbrella Cellular Automata Transforms (CAT). There is a huge number of these transform bases. CAT can be used in the way other mathematical transforms (e.g., Fourier, Discrete Cosine, Laplace, Wavelet, etc.) are utilized. In data-compression applications, the rules and pertinent keys used to generate the CA are selected to favor those that yield basis functions with the best information-packing characteristics. On the other hand, for encryption the selection is biased towards those with the tendency to yield an avalanche effect. In the latter case the transform process must be error-free.     

Frequent itemset mining using cellular learning automata

A core issue of the association rule extracting process in the data mining field is to find the frequent patterns in the database of operational transactions. If these patterns discovered, the decision making process and determining strategies in organizations will be accomplished with greater precision. Frequent pattern is a pattern seen in a significant number of transactions. Due to the properties of these data models which are unlimited and high-speed production, these data could not be stored in memory and for this reason it is necessary to develop techniques that enable them to be processed online and find repetitive patterns. Several mining methods have been proposed in the literature which attempt to efficiently extract a complete or a closed set of different types of frequent patterns from a dataset. In this paper, a method underpinned upon Cellular Learning Automata (CLA) is presented for mining frequent itemsets. The proposed method is compared with Apriori, FP-Growth and BitTable methods and it is ultimately concluded that the frequent itemset mining could be achieved in less running time. The experiments are conducted on several experimental data sets with different amounts of minsup for all the algorithms as well as the presented method individually. Eventually the results prod to the effectiveness of the proposed method.     

Image processing using 3-state cellular automata

This paper describes the application of cellular automata (CA) to various image processing tasks such as denoising and feature detection. Whereas our previous work mainly dealt with binary images, the current work operates on intensity images. The increased number of cell states (i.e. pixel intensities) leads to a vast increase in the number of possible rules. Therefore, a reduced intensity representation is used, leading to a three state CA that is more practical. In addition, a modified sequential floating forward search mechanism is developed in order to speed up the selection of good rule sets in the CA training stage. Results are compared with our previous method based on threshold decomposition, and are found to be generally superior. The results demonstrate that the CA is capable of being trained to perform many different tasks, and that the quality of these results is in many cases comparable or better than established specialised algorithms.     

Design of materials using hybrid cellular automata

This paper presents a new approach for the topological design of materials with extreme properties. The method is based on hybrid cellular automaton (HCA), which is an implicit optimization technique that uses local rules to update design variables iteratively until meeting the described optimality conditions. By means of an energy-based homogenization approach, the effective properties of the considered material are calculated in terms of element mutual energies. By this method, no sensitivity information is required to find the optimal topology for the considered design objectives: bulk modulus, shear modulus, and negative Poisson’s ratio. The proposed method is validated by a series of numerical examples.