DEEPFAKE CLI：使用FPGA的加速Deepfake检测
DEEPFAKE CLI: Accelerated Deepfake Detection using FPGAs
摘要：
由于更大数据集的可用性和生成模型的最新改进，每天都在制作更逼真的Deepfake视频。人们每天在社交媒体平台上消费大约10亿小时的视频，这就是为什么阻止虚假视频的传播非常重要，因为它们可能具有破坏性，危险性和恶意性。在deepfake分类领域已经有了显着的改进，但deepfake检测和推理仍然是一项艰巨的任务。为了解决这一问题，本文提出了一种新的DEEPFAKE C-L-I（分类-定位-推理），其中我们探索了使用FPGA加速量化Deepfake检测模型的想法，因为与通用GPU相比，它们具有最大的并行性和能效。在本文中，我们使用了轻量级的MesoNet与X-YNet结构，并在VCK 5000 FPGA上加速它，由最先进的VC 1902 Versal架构提供支持，该架构使用AI，DSP和Adaptable Engines进行加速。我们已经将我们的推理速度与其他最先进的推理节点进行了基准测试，在VCK 5000上获得了316.8 FPS，同时保持了93%的准确率。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
