The use of deep neural networks in modern surveillance systems enables real-time object detection, facial recognition, and anomaly detection, but they remain vulnerable to adversarial attacks, creating critical security risks. This survey reviews detection methods tailored for real-time surveillance, categorizing domain-specific attacks including gradient-based methods (FGSM, PGD, C&W), physical patches, and temporal attacks on video data. We evaluate detection approaches across six categories: feature-based (LID, frequency analysis), reconstruction-based (autoencoders, GANs), auxiliary model-based, uncertainty-based (Bayesian Networks, MIAD), steganalysis-based, and attention-based (ViTGuar... More >