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@article{176001,
        author = {Mr. R. D. Dhongade and Payal Rahul Bhongale and Tanishka Yogesh Katke and Vaishnavi Ganesh Bhagwat and Suzana Khalid Barmare},
        title = {Image Steganography: Comparative Analysis of Traditional and Deep Learning Techniques},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {6423-6427},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=176001},
        abstract = {In today's digital landscape, ensuring secure and covert communication has become more critical than ever. Image steganography plays a vital role in this by embedding secret information within images in a way that makes detection nearly impossible. Traditional techniques like Least Significant Bit (LSB) substitution and transform domain methods have long been used for this purpose, offering simplicity and reliability. However, these approaches often face limitations in terms of embedding capacity, robustness against attacks, and security. With the rise of deep learning, image steganography has undergone a transformation. Advanced models such as autoencoders, Generative Adversarial Networks (GANs), and Convolutional Neural Networks (CNNs) now enable more efficient and imperceptible data hiding. These methods enhance the invisibility, resilience, and payload capacity of steganographic systems while maintaining high image fidelity. Despite these advancements, challenges such as high computational costs, training complexity, and dataset requirements persist. This research paper delves into the advancements in image steganography, focusing on deep learning innovations while addressing their strengths, weaknesses, and real-world applications. We analyze how these modern approaches compare to traditional methods and explore potential hybrid techniques that can bridge existing gaps. By investigating recent developments, we aim to provide insights into the future of secure communication through image steganography.},
        keywords = {Convolutional Neural Networks, Deep Learning, Data Hiding, Generative Adversarial Networks, Image Steganography, Secure Communication.},
        month = {April},
        }