论文标题
Bladerunner：合成（AI生成的）样式面孔的快速对策
BLADERUNNER: Rapid Countermeasure for Synthetic (AI-Generated) StyleGAN Faces
论文作者
论文摘要
Stylegan是NVIDIA制造的开源张量实现。它彻底改变了高质量的面部图像生成。但是，人工智能 /机器学习（AI / ML）算法的这种民主化使敌对威胁参与者能够在社交媒体平台上建立网络角色或袜子 - 派对账户。这些超现实的合成面。本报告调查了AI/ML与网络和信息操作的相关性。 AI/ML算法的扩散导致了深层和不真实的社交媒体帐户的上升。在战略和运营环境中分析威胁。现有的识别合成面的方法存在，但它们依靠人类在视觉上审查每张照片是否存在矛盾。但是，通过使用DLIB 68-Landmark预训练的文件，可以通过在StyleGAN图像中利用重复行为来分析和检测合成面。 Project Blade Runner包括对抗StyleGAN图像所需的两个脚本。通过纸质放置作为分析仪，可以从刮擦图像样本中得出攻击指标（IOA）。这些IOA可以回到起作用的探测器中，以识别实时操作样本中的合成面。 Blade Runner的OpenSource副本可能缺乏其他单元测试和一些功能，但是开源副本是一个编辑版本，更精细，更好地优化，并且是信息安全社区的概念验证。所需的最终状态将是逐步添加自动化，以与其封闭形源的前身保持在良好状态。
StyleGAN is the open-sourced TensorFlow implementation made by NVIDIA. It has revolutionized high quality facial image generation. However, this democratization of Artificial Intelligence / Machine Learning (AI/ML) algorithms has enabled hostile threat actors to establish cyber personas or sock-puppet accounts in social media platforms. These ultra-realistic synthetic faces. This report surveys the relevance of AI/ML with respect to Cyber & Information Operations. The proliferation of AI/ML algorithms has led to a rise in DeepFakes and inauthentic social media accounts. Threats are analyzed within the Strategic and Operational Environments. Existing methods of identifying synthetic faces exists, but they rely on human beings to visually scrutinize each photo for inconsistencies. However, through use of the DLIB 68-landmark pre-trained file, it is possible to analyze and detect synthetic faces by exploiting repetitive behaviors in StyleGAN images. Project Blade Runner encompasses two scripts necessary to counter StyleGAN images. Through PapersPlease acting as the analyzer, it is possible to derive indicators-of-attack (IOA) from scraped image samples. These IOAs can be fed back into AmongUs acting as the detector to identify synthetic faces from live operational samples. The opensource copy of Blade Runner may lack additional unit tests and some functionality, but the open-source copy is a redacted version, far leaner, better optimized, and a proof-of-concept for the information security community. The desired end-state will be to incrementally add automation to stay on-par with its closed-source predecessor.
