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.
翻译:由于有了更大的数据集和基因模型的最近改进,我们每天都在制作更现实的Deepfake视频。人们每天在社交媒体平台上消耗约10亿小时的视频,因此,必须阻止假视频的传播,因为这些视频可能具有破坏性、危险性和恶意性。在深假分类领域有了重大改进,但深假检测和推断仍是一项艰巨的任务。为了解决本文件中的问题,我们提议了一个新的DEEEEPFAK C-L-I(分类-本地化-感知),其中我们探讨了如何利用FPGA加速量化的深法检测模型的想法,因为与通用GPPS相比,它们具有最大的平行能力和能源效率。在本文中,我们使用EFF-YNet的光线网,并加快了VCK5000 FPGA。我们用州级的VC1902VS-VS-Vserence的动力,我们用AI、DSP和可调控的引擎来加速运行。我们用93年的FPSC的州-PS。