Enhanced CNN Approaches for Multi-Image Embedding in Image Steganography

We unveil a convolutional neural network (CNN)-architectured steganographic model aimed at concealing multiple secret images in a single cover image, seeking to enhance payload capacity and minimize any encoding or decoding errors. We use a method that employs CNNs for encoding and decoding, harnessing a multi-scale encoder and a key-based decryption approach for increased security. Steganography is accurately and successfully achieved at two and three image levels by the model. For the use of two-image steganography, an accuracy of 95.16% was achieved for the encoded cover image, and decoding accuracies of 97.16% and 97.28% for secret images 1 and 2, respectively. In the three-image steganography, the accuracy of the coded cover image stood at 93.84%, while the decoded secret images achieved accuracies of 96.39%, 94.82%, and 94.76%. We boosted security by employing cryptographic techniques such as AES and ChaCha20 and instituted key integration at the architectural level. Our findings show the competent encoding and decoding of a range of secret images with enhanced security and noteworthy precision.

Recommended citation: Md. Irtiza Hossain, Samiul Kadir, Farhan Ishraq Fagun, Ishtiaq Samiul, Rafi Zaman Saukhin. (2024). "Enhanced CNN Approaches for Multi-Image Embedding in Image Steganography." 2024 International Conference on Information and Communication Technologies (ICICT).
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