BLADERUNNER: Rapid Countermeasure for Synthetic (AI-Generated) StyleGAN Faces

TL;DR

This paper introduces Blade Runner, a rapid and open-source countermeasure leveraging facial landmark analysis to detect AI-generated StyleGAN faces, addressing the rise of deepfake threats in social media security.

Contribution

It presents a novel, lightweight, open-source tool that exploits facial landmark patterns to efficiently identify synthetic faces generated by StyleGAN.

Findings

Effective detection of StyleGAN synthetic faces using landmark analysis.

Open-source scripts enable rapid deployment and testing.

Potential for automation to improve detection accuracy over time.

Abstract

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.