The field of video analysis and security is rapidly evolving, with a focus on developing innovative methods to detect and prevent cyber-based attacks and intellectual property theft. Recent research has explored the use of optical side-channels to reverse engineer 3D print instructions from video recordings, highlighting the need for increased security measures in the 3D printing industry. Meanwhile, video anomaly detection has seen significant advancements, with the introduction of autoregressive denoising score matching mechanisms and perceptual straightening techniques. These methods have shown state-of-the-art performance in detecting out-of-distribution anomalies and distinguishing between natural and AI-generated videos. Another area of research has focused on deepfake video detection, with approaches exploiting pixel-wise temporal inconsistencies and using joint transformer modules to integrate temporal frequency features with spatio-temporal context features. Noteworthy papers include: One Video to Steal Them All, which demonstrates the feasibility of reverse engineering 3D print instructions from video recordings with high accuracy. AI-Generated Video Detection via Perceptual Straightening, which proposes a novel approach to distinguish natural from AI-generated videos using perceptual straightening techniques. Beyond Spatial Frequency, which introduces a deepfake video detection approach that exploits pixel-wise temporal inconsistencies, providing robust performance across diverse detection scenarios.