  Because of the availability of larger datasets and recent improvements in the
generative model, more realistic Deepfake videos are being produced each day.
People consume around one billion hours of video on social media platforms
every day, and thats why it is very important to stop the spread of fake videos
as they can be damaging, dangerous, and malicious. There has been a significant
improvement in the field of deepfake classification, but deepfake detection and
inference have remained a difficult task. To solve this problem in this paper,
we propose a novel DEEPFAKE C-L-I (Classification-Localization-Inference) in
which we have explored the idea of accelerating Quantized Deepfake Detection
Models using FPGAs due to their ability of maximum parallelism and energy
efficiency compared to generalized GPUs. In this paper, we have used light
MesoNet with EFF-YNet structure and accelerated it on VCK5000 FPGA, powered by
state-of-the-art VC1902 Versal Architecture which uses AI, DSP, and Adaptable
Engines for acceleration. We have benchmarked our inference speed with other
state-of-the-art inference nodes, got 316.8 FPS on VCK5000 while maintaining
93\% Accuracy.
