Published Paper Details:

- Steganography is the process of hiding secret information, be it text, images, or videos, within digital media in a manner imperceptible to human eyes. The rise of deep learning technology has marked a powerful advancement in various applications, including steganography, attracting significant attention and fostering extensive research. This paper presents a concise review of diverse steganography technologies, encompassing both traditional methods and the latest emerging deep learning techniques. Spatial domain technologies like Least Significant Bit (LSB), and transform domain technologies such as Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT), are explored alongside deep learning technologies like Convolutional Neural Networks (CNN) and Generative Adversarial Networks (GAN) in the context of steganography. The discussion extends to various datasets used in steganography research, including BossBase, ImageNet, and MNIST. This paper aims to provide valuable insights for fellow researchers by reviewing contemporary trends and offering potential directions for the future of steganography research.