Ramadhan J. Mstafa

Bio: Ramadhan J. Mstafa is an academic researcher from University of Bridgeport. The author has contributed to research in topics: Steganography & Steganography tools. The author has an hindex of 13, co-authored 23 publications receiving 496 citations. Previous affiliations of Ramadhan J. Mstafa include University of Zakho.

A Robust and Secure Video Steganography Method in DWT-DCT Domains Based on Multiple Object Tracking and ECC

Abstract: Over the past few decades, the art of secretly embedding and communicating digital data has gained enormous attention because of the technological development in both digital contents and communication. The imperceptibility, hiding capacity, and robustness against attacks are three main requirements that any video steganography method should take into consideration. In this paper, a robust and secure video steganographic algorithm in discrete wavelet transform (DWT) and discrete cosine transform (DCT) domains based on the multiple object tracking (MOT) algorithm and error correcting codes is proposed. The secret message is preprocessed by applying both Hamming and Bose, Chaudhuri, and Hocquenghem codes for encoding the secret data. First, motion-based MOT algorithm is implemented on host videos to distinguish the regions of interest in the moving objects. Then, the data hiding process is performed by concealing the secret message into the DWT and DCT coefficients of all motion regions in the video depending on foreground masks. Our experimental results illustrate that the suggested algorithm not only improves the embedding capacity and imperceptibility but also enhances its security and robustness by encoding the secret message and withstanding against various attacks.

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.

A highly secure video steganography using Hamming code (7, 4)

Abstract: Due to the high speed of internet and advances in technology, people are becoming more worried about information being hacked by attackers. Recently, many algorithms of steganography and data hiding have been proposed. Steganography is a process of embedding the secret information inside the host medium (text, audio, image and video). Concurrently, many of the powerful steganographic analysis software programs have been provided to unauthorized users to retrieve the valuable secret information that was embedded in the carrier files. Some steganography algorithms can be easily detected by steganalytical detectors because of the lack of security and embedding efficiency. In this paper, we propose a secure video steganography algorithm based on the principle of linear block code. Nine uncompressed video sequences are used as cover data and a binary image logo as a secret message. The pixels' positions of both cover videos and a secret message are randomly reordered by using a private key to improve the system's security. Then the secret message is encoded by applying Hamming code (7, 4) before the embedding process to make the message even more secure. The result of the encoded message will be added to random generated values by using XOR function. After these steps that make the message secure enough, it will be ready to be embedded into the cover video frames. In addition, the embedding area in each frame is randomly selected and it will be different from other frames to improve the steganography scheme's robustness. Furthermore, the algorithm has high embedding efficiency as demonstrated by the experimental results that we have obtained. Regarding the system's quality, the Pick Signal to Noise Ratio (PSNR) of stego videos are above 51 dB, which is close to the original video quality. The embedding payload is also acceptable, where in each video frame we can embed 16 Kbits and it can go up to 90 Kbits without noticeable degrading of the stego video's quality.

A high payload video steganography algorithm in DWT domain based on BCH codes (15, 11)

Abstract: Video steganography has become a popular topic due to the significant growth of video data over the Internet. The performance of any steganography algorithm depends on two factors: embedding efficiency and embedding payload. In this paper, a high embedding payload of video steganography algorithm has been proposed based on the BCH coding. To improve the security of the algorithm, a secret message is first encoded by BCH(n, k, t) coding. Then, it is embedded into the discrete wavelet transform (DWT) coefficients of video frames. As the DWT middle and high frequency regions are considered to be less sensitive data, the secret message is embedded only into the middle and high frequency DWT coefficients. The proposed algorithm is tested under two types of videos that contain slow and fast motion objects. The results of the proposed algorithm are compared to both the Least Significant Bit (LSB) and [1] algorithms. The results demonstrate better performance for the proposed algorithm than for the others. The hiding ratio of the proposed algorithm is approximately 28%, which is evaluated as a high embedding payload with a minimal tradeoff of visual quality. The robustness of the proposed algorithm was tested under various attacks. The results were consistent.

Compressed and raw video steganography techniques: a comprehensive survey and analysis

Abstract: In the last two decades, the science of covertly concealing and communicating data has acquired tremendous significance due to the technological advancement in communication and digital content. Steganography is the art of concealing secret data in a particular interactive media transporter, e.g., text, audio, image, and video data in order to build a covert communication between authorized parties. Nowadays, video steganography techniques have become important in many video-sharing and social networking applications such as Livestreaming, YouTube, Twitter, and Facebook because of the noteworthy development of advanced video over the Internet. The performance of any steganographic method ultimately relies on the imperceptibility, hiding capacity, and robustness. In the past decade, many video steganography methods have been proposed; however, the literature lacks of sufficient survey articles that discuss all techniques. This paper presents a comprehensive study and analysis of numerous cutting edge video steganography methods and their performance evaluations from literature. Both compressed and raw video steganography methods are surveyed. In the compressed domain, video steganography techniques are categorized according to the video compression stages as venues for data hiding such as intra frame prediction, inter frame prediction, motion vectors, transformed and quantized coefficients, and entropy coding. On the other hand, raw video steganography methods are classified into spatial and transform domains. This survey suggests current research directions and recommendations to improve on existing video steganography techniques.

P-SEP: a prolong stable election routing algorithm for energy-limited heterogeneous fog-supported wireless sensor networks

Abstract: Energy efficiency is one of the main issues that will drive the design of fog-supported wireless sensor networks (WSNs). Indeed, the behavior of such networks becomes very unstable in node's heterogeneity and/or node's failure. In WSNs, clusters are dynamically built up by neighbor nodes, to save energy and prolong the network lifetime. One of the nodes plays the role of Cluster Head (CH) that is responsible for transferring data among the neighboring sensors. Due to pervasive use of WSNs, finding an energy-efficient policy to opt CHs in the WSNs has become increasingly important. Due to this motivations, in this paper, a modified Stable Election Protocol (SEP), named Prolong-SEP (P-SEP) is presented to prolong the stable period of Fog-supported sensor networks by maintaining balanced energy consumption. P-SEP enables uniform nodes distribution, new CH selecting policy, and prolong the time interval of the system, especially before the failure of the first node. P-SEP considers two-level nodes' heterogeneities: advanced and normal nodes. In P-SEP, the advanced and normal nodes have the opportunity to become CHs. The performance of the proposed approach is evaluated by varying the various parameters of the network in comparison with other state-of-the-art cluster-based routing protocols. The simulation results point out that, by varying the initial energy and node heterogeneity parameters, the network lifetime of P-SEP improved by 31, 29, 20 and 40 % in comparison with SEP, Low-Energy Adaptive Clustering Hierarchy with Deterministic Cluster-Head Selection (LEACH-DCHS), Modified SEP (M-SEP) and an efficient modified SEP (EM-SEP), respectively.

Activity Recognition Using Temporal Optical Flow Convolutional Features and Multilayer LSTM

Abstract: Nowadays digital surveillance systems are universally installed for continuously collecting enormous amounts of data, thereby requiring human monitoring for the identification of different activities and events. Smarter surveillance is the need of this era through which normal and abnormal activities can be automatically identified using artificial intelligence and computer vision technology. In this paper, we propose a framework for activity recognition in surveillance videos captured over industrial systems. The continuous surveillance video stream is first divided into important shots, where shots are selected using the proposed convolutional neural network (CNN) based human saliency features. Next, temporal features of an activity in the sequence of frames are extracted by utilizing the convolutional layers of a FlowNet2 CNN model. Finally, a multilayer long short-term memory is presented for learning long-term sequences in the temporal optical flow features for activity recognition. Experiments 1 1 https://github.com/Aminullah6264/Activity_Rec_ML-LSTM . are conducted using different benchmark action and activity recognition datasets, and the results reveal the effectiveness of the proposed method for activity recognition in industrial settings compared with state-of-the-art methods.

A Robust and Secure Video Steganography Method in DWT-DCT Domains Based on Multiple Object Tracking and ECC

Abstract: Over the past few decades, the art of secretly embedding and communicating digital data has gained enormous attention because of the technological development in both digital contents and communication. The imperceptibility, hiding capacity, and robustness against attacks are three main requirements that any video steganography method should take into consideration. In this paper, a robust and secure video steganographic algorithm in discrete wavelet transform (DWT) and discrete cosine transform (DCT) domains based on the multiple object tracking (MOT) algorithm and error correcting codes is proposed. The secret message is preprocessed by applying both Hamming and Bose, Chaudhuri, and Hocquenghem codes for encoding the secret data. First, motion-based MOT algorithm is implemented on host videos to distinguish the regions of interest in the moving objects. Then, the data hiding process is performed by concealing the secret message into the DWT and DCT coefficients of all motion regions in the video depending on foreground masks. Our experimental results illustrate that the suggested algorithm not only improves the embedding capacity and imperceptibility but also enhances its security and robustness by encoding the secret message and withstanding against various attacks.