Real-Time Advanced Computational Intelligence for Deep Fake Video Detection

Real-time Deep Video Deinterlacing

Fig. 1. (a) Input interlaced frames. (b) Deinterlaced results generated by SRCNN [4] re-trained with our dataset. (c) Blown-ups from (b) and (d) respectively. (d) Deinterlaced results generated by our method. The classical super-resolution method SRCNN reconstruct each frame based on a single field and has large information loss. It also follows the conventional translation-invariant assumption...

Real Fake Real Fake

Real - Time Real - World Visual Classi cation { Making Computational Intelligence

An Intelligent Inspection Engine (IIE) for classiication of non-regular shaped objects from images is described and evaluated using real-world data from a waste package sorting application. The entire system is self-organizing. Principal component analysis and additional a priori knowledge on color properties are used for feature extraction. As classiiers growing neural networks provide robustn...

Real-Time Human Detection for Aerial Captured Video Sequences via Deep Models

Human detection in videos plays an important role in various real life applications. Most of traditional approaches depend on utilizing handcrafted features which are problem-dependent and optimal for specific tasks. Moreover, they are highly susceptible to dynamical events such as illumination changes, camera jitter, and variations in object sizes. On the other hand, the proposed feature learn...

Computational Intelligence Clustering for Dynamic Video Watermarking

Multimedia products today broadcast over networks and are typically compressed and transmitted from host to client. Adding watermarks to the compressed domain ensures content integrity, protects copyright, and can be detected without quality degradation. Hence, watermarking video data in the compressed domain is important. This work develops a novel video watermarking system with the aid of com...