首页|Researcher from Tamil Nadu Provides Details of New Studies and Findings in the Area of Pattern Recognition and Artificial Intelligence (Pelican Whale Optimization Enabled Deep Learning Framework for Video Steganography Using Arnold ...)
Researcher from Tamil Nadu Provides Details of New Studies and Findings in the Area of Pattern Recognition and Artificial Intelligence (Pelican Whale Optimization Enabled Deep Learning Framework for Video Steganography Using Arnold ...)
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NETL
NSTL
World Scientific
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on pattern recognition and artificial intelligence is the subject of a new report. According to news reporting from Tamil Nadu, India, by NewsRx journalists, research stated, “Steganography refers to hiding a secret message from various sources, such as images, videos, audio and so on. The advantage of steganography is to avoid data hacking in transmission medium during the transmission of information sources.” Our news journalists obtained a quote from the research from Department of Computer Science and En- gineering: “Video steganography is superior to image steganography since the videos can hide a substantial quantity of secret messages more than the image. Hence, this research introduced the video stereography technique, Arnold Transform with SqueezeNet-based Pelican Whale Optimization Algorithm (AT[Formula: see text]SqueezeNet_PWOA), for concealing the secret image on the video. To hide the secret image on the video, the proposed method follows three steps: key frame and feature extraction, pixel prediction and embedding. The extraction of the key frame process is carried out by the Structural Similarity Index Measure (SSIM), and then the neighborhood features and convolutional neural network (CNN) features are extracted from the frame to improve the robustness of the embedding process. Moreover, the pixel prediction is completed by the SqueezeNet model, wherein the learning factors are tuned by the PWOA. In addition, the embedding process is completed by applying the Arnold transform on the predicted pixel, and the transformed regions are combined with the secret image using the embedding function. Likewise, the extraction process extracts the secret image from the embedded video by substituting the predicted pixel and Arnold transform on the embedded video. The proposed method is used to hide chunks of secret data in the form of video sequences and it improves the performance.”
Department of Computer Science and EngineeringTamil NaduIndiaAsiaCybersecurityMachine LearningPattern Recognition and Artificial Intelligence
NETL
NSTL
World Scientific
