Adaptive multi-cascaded ResNet-based efficient multimedia steganography framework using hybrid mouth brooding fish-emperor penguin optimization mechanism

A massive amount of data is transmitted in the Internet of Things (IoT). Nowadays, the concerning of security issues are the major factor while transferring data through wireless networks. Since, data privacy becomes complicated. In this research work, a newly proposed model for multimedia steganography is developed. Initially, the required video is obtained from the publically available datasets, and then the acquired input is subjected to the Adaptive Discrete Cosine Transformation (DCT) based block process. The optimal blocks are chosen by the Adaptive Multi-cascaded ResNet (AMC-ResNet) model for applying stego data. Here, the parameter optimization takes place in the DCT and ResNet model to enhance the steganography performance via the Mouth Brooding Fish Emperor Penguin Optimization (MBFEPO) derived from the Mouth Brooding Fish Algorithm (MBFA) and Emperor Penguin Optimization Algorithm (EPOA). Finally, the inverse DCT is employed at the blocks to get the final stego video. In the audio steganography phase, the wanted audio is gathered from external websites. The collected data are given to the Short-time Fourier Transform (STFT) to convert into the spectrogram image, and then the spectrogram image is given to the Adaptive DCT block, selecting the block to apply stego data. Thus, the blocks are selected with the utilization of the Adaptive Multi-cascaded ResNet (AMC-ResNet), where the parameters within the DCT and the ResNet are optimized via the same MBFEPO to improve the performance. After, the Inverse ADCT is applied to reconstruct the spectrogram image. Then, the resultant stego audio is obtained by using the Inverse STFT. Finally, several experiments are conducted to estimate the working ability of the proposed steganography model. The outcome of the recommended model shows 12.3%, 52.6%, 12.3%, and 84.3% better performance SFO, HBA, MBFA, and EPOA in terms of median. The recommended model performs superior performance rather than the existing approaches.