COATI OPTIMIZATION ALGORITHM-DRIVEN CONTENT-ADAPTIVE STEGANOGRAPHY WITH TRANSFORMER ATTENTION FOR STATISTICALLY SECURE COVERT COMMUNICATION
We propose a content-adaptive image steganography framework using the Coati Optimization Algorithm (COA) with a transformer-based self-attention cost model for statistically secure covert communication. Existing steganography methods embed secret data independently of the local image content, and are vulnerable to modern steganalysis detectors. We improve on this using a non-local attention cost model (parameterized by texture-aware query, key and value projections) trained with COA to minimize the SRM steganalysis footprint over a training set of cover images. Bit allocation is then performed according to the optimized cost map to drive secret bits to perceptually safe locations, and Hamming(7,4) error-correcting codes are used to guarantee reliable recovery of the payload in the presence of channel attacks. We compare the method to LSB, DCT and PVD baselines across the metrics of imperceptibility (PSNR, SSIM), robustness (BER with JPEG, Gaussian, salt-and-pepper, rotation and median-filter attacks), embedding capacity and steganalysis detectability. An ablation study justifies the importance of both the COA training stage as well as the transformer attention mechanism. We demonstrate convergence of COA against PSO, GA and WOA, showing superior fitness optimization performance. Statistical significance is shown through Wilcoxon signed-rank tests.