Yazar "Wu, Rui" seçeneğine göre listele

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    AEA-NCS: An audio encryption algorithm based on a nested chaotic system
    (Elsevier Ltd., 2022) Wu, Rui; Gao, Suo; Wang, Xingyuan; Liu, Songbo; Li, Qi; Erkan, Uğur; Tang, Xianglong
    Audio information strongly correlates in adjacent times, and the data type of the audio is float, so the traditional encryption algorithms for the image are unsuitable for audio encryption. This paper proposes an audio encryption algorithm based on Chaos, named AEA-NCS. Most 1D maps have a control parameter, and the parameter space in the chaotic state is small. Therefore, a 2D-Logistic-nested-infinite-collapse (2D-LNIC) is proposed by combining an infinite collapse map (1D-ICM) and a logistic map. There are two control parameters in 2D-LNIC, and it exhibits good chaotic performance through the Lyapunov exponent and attractor phase diagram. In the audio encryption algorithm, 2D-LNIC generates the keystream, and the encryption algorithm is a process of scrambling and diffusion simultaneously. This structure increases the security of the algorithm. We evaluate AEA-NCS in ESC-50, and the evaluation results show that AEA-NCS exhibits good performance, significantly reducing the correlation of audio information in adjacent times.
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    Design, Dynamical Analysis, and Hardware Implementation of a Novel Memcapacitive Hyperchaotic Logistic Map
    (IEEE-Inst Electrical Electronics Engineers Inc, 2024) Gao, Suo; Ho-Ching Iu, Herbert; Erkan, Uğur; Şimşek, Cemaleddin; Mou, Jun; Toktaş, Abdurrahim; Wu, Rui
    Currently, discrete memristors are a focal point in the study of chaotic maps. Similar to memristors, memcapacitors-another type of memory circuit component-have not received widespread attention in the design of chaotic maps. In this article, we propose a 4-D memcapacitive hyperchaotic logistic map (4D-MHLM) by integrating memcapacitors with the logistic map. The dynamical behavior of the 4D-MHLM is analyzed using Lyapunov exponent analysis, and the impact of different parameters on system performance is discussed. The complexity of generating pseudo-random sequences with the 4D-MHLM is investigated through complexity analysis, including spectral entropy complexity and C0 complexity. Notably, attractor analysis reveals a unique phenomenon of infinite coexisting attractors within the 4D-MHLM. Finally, the chaotic attractor generated by the 4D-MHLM is successfully implemented on a hardware platform. Theoretical analysis and digital circuit implementation results indicate that the 4D-MHLM exhibits rich dynamical behavior and higher complexity, offering significant value for practical applications.
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    Development of a video encryption algorithm for critical areas 2D extended Schaffer function and neural networks
    (Elsevier Science Inc, 2024) Gao, Suo; Liu, Jiafeng; Iu, Herbert Ho-Ching; Erkan, Uğur; Zhou, Shuang; Wu, Rui; Tang, Xianglong
    This paper proposes an encryption algorithm for crucial areas of a video based on chaos and a neural network, which SVEA (Selective Video Encryption Algorithm). The critical areas of each frame in a video are extracted by deep learning to the encryption system. A one-step encryption algorithm is used to encrypt these critical areas based on chaos, where scrambling and diffusion are simultaneously performed. A new chaotic system 2D extended Schaffer function map (2DESFM) is utilized in the encryption system, inspired by the Schaffer function. The system has demonstrated excellent performance through Lyapunov exponents (LEs), permutation entropy (PE), the 0-1 test, and other methods. Additionally, to resist chosen plaintext attacks, the secret key is generated by a neural network, with the critical areas of the video as inputs to the neural network. The chaotic system generates the biases and weights for the neural network. We evaluate SVEA on our dataset (Gymnastics at the Olympic Games) and public datasets. SVEA exhibits strong security characteristics compared to state-of-the-art algorithms and reduces time complexity by approximately 51.3%.
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    MSLID-TCN: multi-stage linear-index dilated temporal convolutional network for temporal action segmentation
    (Springer Heidelberg, 2024) Gao, Suo; Wu, Rui; Liu, Songbo; Erkan, Uğur; Toktaş, Abdurrahim; Liu, Jiafeng
    Temporal Convolutional Network (TCN) has received extensive attention in the field of speech synthesis. Many researchers use TCN-based models for action segmentation since both tasks focus on contextual connections. However, TCN can only capture the long-term dependencies of information and ignores the short-term dependencies, which can lead to over-segmentation by dividing a single action interval into multiple action categories. This paper proposes Multi-Stage Linear-Index Dilated TCN (MSLID-TCN) model each of whic layer has an appropriate receptive field, allowing the video's short-term and long-term dependencies to be passed to the next layer, thereby optimizing the over-segmentation problem. MSLID-TCN has a four-stage structure. The first stage is a LID-TCN, while the remaining stages are Single Stage TCNs (SS-TCNs). The I3D feature of the video is used as the input for MSLID-TCN. In the first stage, LID-TCN makes initial predictions on frame features to obtain predicted probability values. In the last three stages, these probability features are used as input to the network where SS-TCN corrects the predicted probability values from the previous stage, ultimately yielding action segmentation results. Comparative experiments show that our model performs excellently on the three datasets: 50salads, Georgia Tech Egocentric Activities (GTEA), and Breakfast.
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    Securing dual-channel audio communication with a 2-D infinite collapse and logistic map
    (IEEE-Inst Electrical Electronics Engineers Inc, 2024) Wu, Rui; Gao, Suo; Iu, Herbert Ho-Ching; Zhou, Shuang; Erkan, Uğur; Toktaş, Abdurrahim; Tang, Xianglong
    To provide robust security measures for audio data during transmission, this article has developed a novel dual-channel audio encryption scheme based on chaos theory. Specifically, a new 2-D chaotic system called 2-D infinite collapse with logistic map (2-D-ICLM) is designed in this article. Compared to traditional 2-D chaotic systems, the 2-D-ICLM exhibits a larger parameter space, complexity, and richness, along with high unpredictability and randomness. These characteristics provide potential advantages and applications in the field of encryption. In the proposed encryption scheme, audio information serves as input to a hash function, which generates the initial values and parameters for the 2-D-ICLM, producing the keystream for the cryptographic system. Considering the correlation between the two channels of audio information, the information from the left and right channels is fused to create a new audio signal for encryption. Scrambling and diffusion processes are performed synchronously in the encryption algorithm, with the ciphertext information from the left channel utilized in the encryption of the right channel audio. The experimental results prove the effectiveness of the suggested audio encryption technique, effectively countering various conventional attack methods and showcasing its robust security features. The correlation of adjacent elements of ciphertext audio is 0.0013, the NSCR and UACI is around 0.9960 and 0.3345, and the efficiency is 0.0003 s/KB.
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    Temporal action segmentation for video encryption
    (Pergamon-Elsevier Science Ltd, 2024) Gao, Suo; Iu, Herbert Ho-Ching; Mou, Jun; Erkan, Uğur; Liu, Jiafeng; Wu, Rui; Tang, Xianglong
    Videos contain temporal information, enabling them to capture the dynamic changes of actions and provide richer visual effects. Traditional video encryption methods involve decomposing videos into frames and encrypting them frame by frame, which results in significant resource consumption. This paper proposes a video encryption method based on temporal action segmentation. This methodology involves the identification and extraction of pivotal frames from a video dataset, followed by the encryption of these significant key frames. This approach serves to enhance the efficacy of the video encryption algorithm. The method consists of three modules. The first module uses temporal action segmentation to classify video frames and extract important frames for the second module's input. The second module encrypts the extracted key frames using a chaos-based encryption algorithm, thereby reducing the time cost of video encryption. The third module outputs the encrypted video. During the encryption process, a large amount of key stream is required. To address this, the paper introduces a new pseudo-random sequence generation method called two-dimensional Gramacy&Lee map (2D-GLM). Comprehensive comparative analysis clearly demonstrates that compared to other systems, 2D-GLM exhibits superior performance and can generate a large number of high-performance pseudo-random sequences. The proposed algorithm is tested on GTEA, and the simulation results demonstrate that it can accomplish video encryption tasks with high security.