SECURE VIDEO CODED SYSTEM MODEL

In this paper, overall system model, shown in Figure (1), of video compression-encryption-transmitter/decompression-dencryption-receiver was designed and implemented. The modified video codec system has used and in addition to compression/decompression, theencryption/decryption video signal by using chaotic neural network (CNN) algorithm was done. Both of quantized vector data and motion vector data have been encrypted by CNN. The compressed and encrypted video data stream has been sent to receiver by using orthogonal frequency division multiplexing (OFDM) modulation technique. The system model was designed according to video signal sample size of 176 × 144 (QCIFstandard format) with rate of 30 frames per second. Overall system model integrates and operates successfully with acceptable performance results

Secure Surveillance Framework for IoT Systems Using Probabilistic Image Encryption

[EN] This paper proposes a secure surveillance framework for Internet of things (IoT) systems by intelligent integration of video summarization and image encryption. First, an efficient video summarization method is used to extract the informative frames using the processing capabilities of visual sensors. When an event is detected from keyframes, an alert is sent to the concerned authority autonomously. As the final decision about an event mainly depends on the extracted keyframes, their modification during transmission by attackers can result in severe losses. To tackle this issue, we propose a fast probabilistic and lightweight algorithm for the encryption of keyframes prior to transmission, considering the memory and processing requirements of constrained devices that increase its suitability for IoT systems. Our experimental results verify the effectiveness of the proposed method in terms of robustness, execution time, and security compared to other image encryption algorithms. Furthermore, our framework can reduce the bandwidth, storage, transmission cost, and the time required for analysts to browse large volumes of surveillance data and make decisions about abnormal events, such as suspicious activity detection and fire detection in surveillance applications.This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. 2016R1A2B4011712). Paper no. TII-17-2066.Muhammad, K.; Hamza, R.; Ahmad, J.; Lloret, J.; Wang, H.; Baik, SW. (2018). Secure Surveillance Framework for IoT Systems Using Probabilistic Image Encryption. IEEE Transactions on Industrial Informatics. 14(8):3679-3689. https://doi.org/10.1109/TII.2018.2791944S3679368914

Encryption of MPEG-2 Video Signal based on Chaotic Neural Network

In this paper, a cipher system based on chaotic neural network (CNN) is used to encrypt and construct a stream cipher of compressed MPEG-2 video signal. The symmetric cipher algorithm transforms the plaintext compressed video data) into the unintelligible form under the control of key; this algorithm has high security and simple architecture with low cost hardware. However, if the size of neural network is increased, the required execution time for CNN encryption and decryption process will be decreased. The whole system model can keep the original file and provide good video quality and reduce the required bit rate which is very suitable to limited bandwidth channel. The proposed system - is also suitable for secure video transmission applications and wireless multimedia communication