Exploring Artificial Intelligence Security: A Comparative Study of Adversarial Attacks and Steganographic Defenses
K. Prathyusha, J. Vinoj, K. B. Manikandan · 2025
Artificial Intelligence systems are increasingly used in critical areas like finance, healthcare, and autonomous systems, ensuring their security becomes crucial. Artificial Intelligence models are vulnerable to adversarial attacks, where small changes to input data can cause major errors, as well as steganographic methods that hide harmful information within digital content. Recent techniques, such as Generative Adversarial Networks (GANs) for steganography and adversarial training for model hardening, have shown promise but face challenges like computational inefficiency, limited generalizability, and susceptibility to evolving threats. The methodology includes five key steps: synthetic data generation, data preprocessing, multi-layered security assessments, dynamic countermeasures, and cross-domain validation. By using adversarial and steganographic data augmentation, the framework strengthens the Artificial Intelligence's ability to handle real-world threats. The approach also integrates advanced feature extraction and classification methods to improve detection accuracy. The model is tested across various domains, demonstrating its ability to secure AI systems against evolving digital threats while maintaining efficiency and adaptability.