Görüntü steganografisini geliştirmek için AI sistemi önerin

Steganography is one of the methods used for the hidden exchange of information and it art of invisible communication. The confidential communication is concealed so that it cannot be seen by human senses. Steganography and encryption offer an effective means of achieving that privacy. Conventional methods of image steganography tend to start with the first pixel and proceed with subsequent pixels until the last piece of the hidden message is embedded. In order to increase the security of the steganography system, modern trends involve arbitrarily hiding data in images using various clever algorithms. Numerous methods will be used in this thesis to improve image steganography and provide high levels of security systems and exchange the secret information in secure way. By arbitrarily concealing encrypted data in the cover, a proposed image steganography method based on Ant Colony Optimization (ACO) aims to improve geographic image steganography. Data Encryption Standard (DES) is used to encode a secret communication to increase the security of the suggested system. The use DES algorithm With using breadth algorithm to generate the key to increase the complexity against the attacker and difficult way to detect the original message. Cover image separating into groups (n*n) optimum pixels can discovered through the connection between pixels. Each block has the Ant Colony System (ACS) applied to it in order to determine the texture of colour among the pixels in the block and determine which pixel is best to use to embed the encrypted secret message bits. This process is repeated for each subsequent block until the secret message finished. The testing study demonstrates that in terms of quality (MSE and PSNR) and security. The final experimental results between security techniques (steganography and cryptography) and artificial intelligence (ACO (ACS) with security techniques) explains. Ant Colony System (ACS) of the ACO algorithm outperforms R(RGB-LSB). The PSNR in the R(RGB-LSB) technique is 71 and ACO is 83, according to the findings of the contrast between the ACO and R(RGB-LSB).