BLADERUNNER Rapid Countermeasure for Synthetic AI -Generated StyleGAN Faces Adam Dorian Wong

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BLADERUNNER

Rapid Countermeasure for Synthetic (AI-Generated) StyleGAN Faces

Adam Dorian Wong

MIT Lincoln Laboratory

Group 52

01 September 2022

MIT/LL: adam.wong[at]ll.mit.edu

Gmail:

Twitter: @MalwreMorghulis

OTX: MalwareMorghulis

GitHub (Personal): https://github.com/MalwareMorghulis

GitHub (MIT/LL): https://github.com/mit-ll/BLADERUNNER (DOI: 10.5281/zendo.7186014)

DISTRIBUTION STATEMENT A. Approved for public release. Distribution is unlimited.

This material is based upon work supported by the Department of the Air Force under Air Force Contract No. FA8702-15-D-0001. Any opinions, findings, conclusions or

recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the Department of the Air Force.

Delivered to the U.S. Government with Unlimited Rights, as defined in DFARS Part 252.227-7013 or 7014 (Feb 2014). Notwithstanding any copyright notice, U.S. Government

rights in this work are defined by DFARS 252.227-7013 or DFARS 252.227-7014 as detailed above. Use of this work other than as specifically authorized by the U.S.

Government may violate any copyrights that exist in this work.

Adam D. Wong

@MalwareMorghulis -1-

Abstract

StyleGAN is NVIDIA’s open-sourced TensorFlow implementation. 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 DLIBs’ 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.py acting as the analyzer, it is possible to derive indicators-of-attack (IOA) from

scraped image samples. These IOAs can be fed back into among_us.py 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.

Going forward PapersPlease, papersplease.py, or papers_please.py naming schema will be used interchangeably, similar to Blade

Runner and Among Us (AmongUs, among_us.py, or amongus.py).

Adam D. Wong

@MalwareMorghulis -2-

Introduction

Artificial Intelligence (AI) & Machine Learning (ML) are increasing areas of concern in

the realm of cybersecurity. Adversaries are exploiting bleeding-edge technologies to enable cyber

and information operations. Threat actors are leveraging DeepFakes and synthetic facial imagery

to manipulate others. Democratization of AI/ML-based technologies poses a significant and

continual cyber risk to national security. CNN reported that OpenAI refused to release their AI out

of concern for abuse [1]. Open-source reporting suggests that proliferation of AI-generated

imagery has been a key enabler for espionage, trolling, and harassment [2] [3].

DeepFakes remain a significant threat through projection of misinformation (misleading)

and disinformation (deception) campaigns. However, one specific type of fake comes in the form

of AI-generated synthetic facial images. Issues in the Strategic Environment (SE) derive from geo-

political propriety (or lack thereof). International law has not kept up with advances in technology.

For example, the Tallinn Manual has not yet addressed advances in AI/ML and its dangerous

potential in war. The technology has not been adequately regulated either. In Operational

Environments, AI/ML technologies are actively exploited by threat actors seeking to sew discord,

maliciously influence others, or engage in social-engineering activities. People are prone to trust

DeepFakes because they are getting more sophisticated in production and possibly based on

cognitive biases. Social media personas leverage these synthetic photos which is a next-generation

alternative to stolen real-photographs or generic stock images.

Blade Runner leverages pre-trained ML-predictor files to detect StyleGAN images through

exploitable repetitive behaviors and Indicators-of-Attack (IOAs). Future iterations of open-source

Blade Runner will automate certain tasks to stay on-par with its closed-source counterpart.

Adam D. Wong

@MalwareMorghulis -3-

Artificial Intelligence

In 2016, Microsoft created an AI chatbot: Tay on Twitter, to conduct an experiment in

learning “conversations”. The premise leveraged the idea that with more interaction (more data),

AI would act more human through learning. However, Twitter users disrupted Tay-AI’s learning

and radicalize the chatbot [4]. Needless to say, AI learning can easily be disrupted by bad actors.

DeepFake is a portmanteau of “Deep Learning” [ML-technique] and “Fake [News]”. The

term was first observed in 2017 via a Reddit user account called: u/deepfakes [5]. This Redditor

grafted celebrity faces onto pornographic media, but their account now lies devoid of any content

[6]. This same technology has been used in Hollywood to posthumously revive beloved characters

such as with: “Grand Moff Tarkin” and “Princess Leia” in Star Wars: Rogue One [7]. However,

in the hands of threat actors, the technology has been abused to misinform or deceive audiences,

degrade public-image, blackmail or embarrass geopolitical leaders, socially-engineer others, or

sew discord in already chaotic environments [8] [9]. It’s been used in memes where Hollywood

actor Nicholas Cage’s likeness is superimposed onto Harrison Ford’s character in Indiana Jones:

Raiders of the Lost Ark [10]. Nevertheless, the computer science areas of AI/ML are exponentially

advancing. AI-assisted media manipulation will be a contested issue between freedom-of-research

and misuse by hostile actors.

The capabilities have since been cyclically advanced and refined. Due to this nature of

continuous improvement, technology for synthesizing DeepFakes has become common in

industry. The technology has been democratized by different research entities and thus more

openly-available to the public [11]. Academic research at leading technical universities has

compelled the nation to expand in the AI/ML space [12] [13]. In bygone eras, this was called

Adam D. Wong

@MalwareMorghulis -4-

“doctoring” media and “photoshopping” (based on Adobe products). Today, this they are known

as DeepFakes.

About StyleGAN

NVIDIA is a well-respected company making advances in the Graphical Processing Unit

(GPU) and graphics card industry. Their department of research heavily operates in AI/ML-space.

Their research in AI/ML has facilitated unparallel advances in computer-generated graphics. It

must be acknowledged that StyleGAN allows ultra-realistic synthetic faces at 1024x1024

resolution.

Generative Adversarial Networks (GANs). DeepFakes rely on multiple algorithms to

synthesize images. Some images are manipulated through encoding processes to overlay latent

images. However, some use ML-algorithms to intelligently overlap photos. Early forms of

DeepFakes. It should be known that StyleGAN itself is not a malicious tool, nor was the research

itself malicious. The application of StyleGAN by hostile actors is what makes the tool dangerous.

Essentially, GANs rely on generators and discriminators to create these ultra-realistic images.

Evolution of StyleGAN:

• 2018

o NVIDIA proposed StyleGAN as their “official TensorFlow implementation” which

used the Flickr-Faces-High-Quality (FFHQ) dataset to train their neural network

[14] [15]

• 2019

o NVIDIA implemented improvements to the existing StyleGAN to enhance existing

capabilities to generate synthetic imagery as StyleGAN 2 [16].

• 2021

o StyleGAN 3 acknowledges weaknesses of StyleGAN2 where certain features are

placed based on fixed coordinates [17].

o StyleGAN-NADA aims to use text to morph images from one graphic to another

with limited training (such as Nvidia’s example of a dog shifting to The Joker) [18].

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BLADERUNNERRapidCountermeasureforSynthetic(AI-Generated)StyleGANFacesAdamDorianWongMITLincolnLaboratoryGroup5201September2022MIT/LL:adam.wong[at]ll.mit.eduGmail:Twitter:@MalwreMorghulisOTX:MalwareMorghulisGitHub(Personal):https://github.com/MalwareMorghulisGitHub(MIT/LL):https://github.com/mit-ll/BL...

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BLADERUNNER Rapid Countermeasure for Synthetic AI -Generated StyleGAN Faces Adam Dorian Wong

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