Abhijeet Kumar

Anil Kumar Singh

Vinod K. Tiwari

Sanchayita Rajkhowa

Garima Tripathi

This book summarizes fundamentals and advanced topics of green chemistry and highlights the importance and impact of green chemistry over traditional synthetic methods. It discusses about the importance and scope of the catalytic protocols in green chemistry and their application in daily life. Alternate green energy approaches discussed in this book underline the importance of efficiency enhancement with simultaneous energy demand reduction by replacing the dependence on non-renewable energy resources. Various topics covered in this book include green solvents, energy-efficient approach for organic synthesis, catalysis, biocatalysis, and green approach in pharmaceutically important molecules and drugs. The book will be a valuable reference for beginners, researchers, and professionals interested in sustainable green chemistry and their scope in allied fields.

Introduction, Application and Scope

Green Chemistry

Ever since the dot com boom, video has been the most important means of mass communication. According to a recent study from Cisco, videos are expected to reach 82% of the internet traffic by the end of 2022. However, unlike images, video analysis is computationally costly as it demands extracting visual information from the vast amount of pixel values. With such a significant impact on our day-to-day life, it is clear that video understanding will shape the future if we can make it more practical and useful for real-time usage. 2D Convolution Neural Networks (2D CNNs) despite demonstrating state-of-the-art results in terms of accuracy and efficiency for various image understanding tasks, did not achieve great success in video analytics. One possible reason behind the failure is that video signals are quite complex and contain high temporal redundancy. Thus extracting rich spatio-temporal features from the signals to learn the key characteristics of the videos is challenging. However, to remove the spatial and temporal redundancy in the bit-stream, videos are usually streamed and stored in their compressed representation rather than the raw format. Hence, it is intuitive that it would be much more practical if 2D-CNNs learned the visual information directly from the compressed representation of the videos instead of reconstructing the RGB frames. We propose a Deepfake Video Detection Network (DVDNet) that extracts the key information confined in small numbers of frames called Intracoded frames (I-frames) from the compressed representations of the videos. Compared to other techniques that leverage I-frames, motion vectors, and residuals in the compressed domain, our work requires less modality to understand the data. Significant pre-processing time and computational demand reduction by solely working on I-frames make our approach more accessible for a broader range of machines and uses. In our work, we primarily focused on one of the most critical tasks of computer vision in video understanding - deepfake video detection. Synthesizing manipulated videos using Deep Learning (called deepfake videos), is an emerging threat to humankind, thus very important to detect. Therefore, we extensively tested our proposed network architecture on the above-mentioned video understanding task. As a result, we achieved 95.88% accuracy and an AUC score of 0.978 on the celeb-df dataset and 87.50% accuracy and 0.97539 AUC score on the faceforensics++ dataset. Our key contribution is that we reduced computational complexity significantly while achieving 95.88% accuracy on the celeb-df dataset and 87.50% on the faceforensics++ dataset.

Deepfake Video Detection in the Compressed Domain

“Will you ever stop loving her?” Shweta was yelling as Ayaan was walking away. He turned back and looked at Rhea for the last time. “Maybe Yes,” Ayaan murmured to himself and continued walking amidst the heavy snowfall. Ayaan struggles to answer the question. He is bewildered, as he still does not have the answer after fifteen years. He is a simple guy with simple tastes, sitting in the corner, watching it all happen around him in which he cannot participate. Nevertheless, he decides to leave no stone unturned when it comes to grabbing an opportunity to overturn the old sissy life. However, all his planning and plotting for turning his life around is flushed down the gutter when Rhea enters his life. It pushes him off the ledge and throws him into the pit of ambiguity. His past haunts him, because of which he is not able to confess his feelings to the girl he loves. He keeps on encountering her in different phases of his life in the span of fifteen years. Maybe Yes is a saga about love, friendship, and finding oneself.

Abhijeet Kumar

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