Australian study reveals worrying attitudes towards AI-based abuse

ABC NEWS (Australia)

A study at Monash University has shown perpetrators of sexualised deepfake abuse frequently downplayed their actions, and the behaviour becomes normalised among their peer groups. Researchers interviewed both perpetrators and victims in the first-of-its-kind study which aims to increase understanding of the growing problem.

Transcript

00:00Noelle Martin's fight against sexualised deepfake abuse began as a teenager.

00:08It was shock and disgust and images of me from social media had been taken

00:15and doctored into fake intimate images.

00:20Years later and the digital age has evolved, but so have the dangers.

00:25In the last 18 months, reports of this kind of abuse in Australia have doubled.

00:31Melbourne researchers have interviewed 10 perpetrators and 15 victim survivors

00:36to better understand the issue.

00:38Motivations varied from money to curiosity, wanting to cause harm and peer reinforcement.

00:45In one example, Jesse altered and shared sexualised deepfakes of a female colleague

00:50after their relationship ended.

00:52He sent those images to a work-related WhatsApp group and another that included the victim's daughter.

00:58He said,

00:59I was just really surprised in retrospect all the evil thoughts I had to get back at this person.

01:05Another perpetrator simply said,

01:07You have a little godlike buzz of seeing that you're capable of creating something like that.

01:13It was really interesting for me that they could see the harms of sexualised deepfake abuse for other people,

01:19but often not in the context that they'd engaged in the behaviour.

01:23This technology dehumanises women, it degrades women, it humiliates women.

01:28Attacks in an online world with real life impacts.

01:33Captions by Red Bee Media

01:34Captions by Red Bee Media

01:35Kerosene

01:36Write the www.netsoesk.com

01:37Read-map

01:38Eternity

01:39To have aized web product

01:405- 대한 type tips

01:412-team

01:44esser

01:45www.netso- einfach

01:463-team

01:471-2-2-2-3-3

Category

Transcript

Be the first to comment

Recommended