An improved image steganography scheme based on deep learningapproach and quadruple security layers

This thesis intends a fresh approach for the improvement of dataset secreting in images via the ACO algorithm. Digital image steganography requires a balance between two essential goals: this provides the maximum optimization of the concealment of data; it would also reduce the possibility of the image from existence noticed via optimizing the quality of the original image to be concealed. Many current steganographic methods tend to sacrifice the goals above and below at some point. The thesis introduces the "ACO-LSB" approach, which is designed to enhance embedding capacity using a gray-scale shelter image to hold secret dataset through adding an extra bit-pair in byte (b) to make a checksum of the integrity of the image or a check sum of the hidden message. The method encrypts secret information as the pairs of bits and embeds into the uncompressed images in grey scale. The algorithm used in the ACO is the adaptive scanning to find pixel locations and increase the data embedding capacity while decreasing the strong impact on the image quality. Otherwise, the specific pheromone values are changed in a cyclical fashion to avoid problems with stagnation in the context of the overall optimization process – the values should be ideal for proper selection of pixels. The performance results of the ACO-LSB method are outstanding and this research confirms that they enabled enhancement in the subsequent image embodiment, with up to a 30% increase in embedding capacity compared to traditional methods. Technology achieves an average maximum Peak Signal-to-Noise Ratio (PSNR) of (40.5) dB and Structural Similarity Index (SSIM) of (0.98). Furthermore, Methodology shows strong resistance to detection, reducing detection rates by 20%. The model was implemented using MATLAB R2023a and tested on a publicly available dataset of 1000 gray-scale pictures, providing strong evidence of its effectiveness.