Robust and Secured Data Hiding Methodology over Digital Images using Deep Learning Enabled Steganographic Norms
Keywords:Data Hiding, Steganography, Data Embedding, Information Hiding, Digital Watermarking, Multisource
Steganography, as opposed to cryptography, is an art form for concealing information in a way that does not draw attention. Since ancient times, people have used this method to communicate and conceal sensitive information from strangers or hackers. Multimedia information is widely utilised as a result of the quick growth of computer networks, and digital media security has received a lot of attention. The facts of the case will be distorted by the manipulated photo used as forensic evidence, and social media photos that have been maliciously altered may harm the persons involved. Information-based digital image self-recovery concealing is used to assure the veracity and integrity of digital media. We suggest a multisource data-hiding system (MDHS), which extracts hidden information on the recipient's side and produces content that is jointly decided by the hidden information sent by all senders. Based on deep learning, this research draws a general conclusion on visual information hiding. Additionally, the suggested approach has the potential to enable lossless data recovery in the event of damage using the dynamic range adjustment technique. Modern information masking techniques based on deep learning are examined and shown. Simulation findings show that the suggested approach outperforms existing cutting-edge strategies in terms of payload and imperceptibility.
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