The field of secure data transmission and adversarial attack detection is rapidly evolving, with a focus on developing innovative methods to protect against increasingly sophisticated threats. Researchers are exploring new approaches to encode and decode covert data, utilizing techniques such as generative adversarial networks and conditional Wasserstein generative adversarial networks to generate synthetic data and improve detection accuracy. Additionally, there is a growing concern about the vulnerability of deep learning-based systems to adversarial attacks, and efforts are being made to develop proactive defense mechanisms, such as adversarial training, to enhance model robustness. Noteworthy papers include: Efficient Blockchain-based Steganography via Backcalculating Generative Adversarial Network, which proposes a generic blockchain-based steganography framework to enhance channel capacity. Adversarial Attacks on Deep Learning-Based False Data Injection Detection in Differential Relays, which demonstrates the vulnerability of deep learning models to adversarial attacks and introduces adversarial training as a defense mechanism.