Gary Huestis was recently interviewed in a Unite AI article titled “Gary Huestis, Owner & Director Of Powerhouse Forensics – Interview Series.”
Huestis was asked to talk about his experience with deepfakes, digital forensics, and more.
Interview Q&A With Unite AI
What initially attracted you to computer science?
I was interested in computers at a very young age. Computers as we know them today were not even around back then, they were more like gaming consoles. My first “computer” was an 8-bit Atari 400 that I got in 1980 at the age of 11. By the time I was 13, I was writing programs for the Atari company through the Atari Program Exchange (APX), and my interest in computers continued to grow from there.
Can you discuss your personal journey and how you eventually became a digital forensic investigator?
I had an IT Support company in the 1990’s, and several of my clients were attorneys. As court cases moved from paper and printouts to computers and digital, I would help the attorneys with the computer and digital data related cases. As I began to testify as the computer expert in those cases one of the attorneys suggested that I look into getting certified in the new field of digital forensics, so that’s what I did.
Do you remember the first time you encountered a deepfake? What was your initial reaction to it?
The term deepfake does not have a clear definition, and I think that many images that were called photoshopped several years ago can fit that category. I think the first time I saw deepfake technology was the movie Forrest Gump where Forrest meets John F Kennedy. I remember thinking that was cool and about how the technology can grow to the point where movies can replace human actors and actresses with digital versions.
With Generative AI deepfakes are becoming increasingly convincing, what are some of the associated cybercrimes?
Using deepfakes to commit cybercrimes is almost limitless. As the technology gets better and easier to use, deepfakes can be used to “show” people doing things that didn’t happen, change the identity of people and convince individuals that trusted friend, colleague or family member they are interacting with is actually a deepfake. We have seen cases where the audio technology of deepfakes was used to make the CFO of a company think his boss asked him to transfer a large sum of money to a bank account when it was actually a cybercriminal.
DeepFakes and disinformation are also going to be a huge concern for manipulating elections, how should social media companies respond to this societal threat?
Implement deepfake detection technology to attempt to detect videos that are deepfakes and either clearly mark them as fake or block them from being viewed. There should also be an option for users of the social media to report videos as fake. Depending on the content, perform some fact checking to see if the video is real.
What are your views on AI transparency, and the need to properly identify deepfakes?
There are some scenarios where deepfake technology is used for entertainment purposes and humor that I don’t think it is necessary to identify deepfakes. But in deepfakes intended to misrepresent a person or scenario, my view is that deepfakes should be identified and labeled as manipulated or fake.
What are some ways that AI is being used to assist in detecting DeepFakes?
AI technology is being used to assist in detecting deepfakes by analyzing aspects of videos that are caused by using deepfake technology such as inconsistent shading or artifacts between the subject and the background. This is consistently changing as the deepfake technology is getting better.
Can you share some insights on what exactly is Deepfake forensics?
Deepfake forensics is the analysis of media with the goal of determining if the media has been created or altered using deepfake technology.
What are the deepfake forensics solutions that are currently offered at Powerhouse Forensics?
At Powerhouse Forensics we offer services to analyze photos, audio and video to determine if the media has been edited, altered or manipulated from its original source.
For more information, refer to the Unite AI article.
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