This paper presents a deep learning-based approach for image steganography in single-frame GIFs using a dual-network architecture. The proposed method introduces H-Net for embedding and R-Net for extraction, both designed as convolutional neural networks (CNNs) trained to maximize imperceptibility and recovery accuracy. Unlike traditional palette sort-based steganographic methods, which suffer from limited hiding capacity and noticeable visual artifacts, the deep learning model significantly improves performance on standard benchmarks. Tested on a dataset of 16,000 images sourced from ImageNet, the method achieves an average PSNR of 34.3 ± 0.6 dB and an SSIM of 0.969 ± 0.010, indicating minimal visual distortion and high structural similarity. The system supports real-time inference, demonstrates robustness to image degradation, and allows concealment of full-resolution RGB images. Additional analyses, including ablation studies and comparison with recent deep learning steganographic models, confirm the method’s superiority. Ethical implications and practical use cases are also discussed. This work offers a secure and scalable solution for covert image communication.