Artificial Intelligence: The Rise of Generative Adversarial Networks (GANs) in Deepfake Cybercrime
By feeding GANs and autoencoders large volumes of video footage and photos, it is possible to create digital representations of targets individuals wish to impersonate.
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Today we highlight the following:
AI: The Rise of Generative Adversarial Networks (GANs) in Deepfake Cybercrime
CURATED UPDATES: Machine Models, AI Applications in Finace, Infrastructure & Middleware
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AI: The Rise of Generative Adversarial Networks (GANs) in Deepfake Cybercrime
There has been a 550% increase in the amount of deepfake content globally across all industries from 2019 to 2023. Ranging from identity fraud to fake news, synthetic pornography, and financial scams, deepfake cybercrime is ranked as the most serious AI crime threat by experts.
Exemplifying the modern deepfake issue is the recent case of cybercriminals impersonating the chief financial officer and other executives of a Hong Kong company during a video conference call. They successfully convinced a finance department employee to perform multiple fraudulent transactions totaling $25.6M directed to accounts controlled by the criminals.
At the heart of this rise in deepfake technology is the refinement of generative adversarial networks (GANs) and autoencoders. GANs employ two neural networks - a generator and a discriminator - that work against each other to refine the creation of fake images or videos until they become indistinguishable from real ones. Autoencoders, consisting of an encoder and decoder, are used to precisely reconstruct human faces with altered expressions and angles by combining the facial features of different people.
By feeding GANs and autoencoders large volumes of video footage and photos, it is possible to create digital representations of targets individuals wish to impersonate.
While deepfake technology primarily earns negative media attention, there are positive applications. Apple’s latest release, the Vision Pro headset, powered by visionOS, exemplifies such use cases.
The Apple Vision Pro Headset produces 3D holographic avatars, “Personas,” using a similar deepfake process. The headset’s sensors, IR cameras, lidar sensors, and 3D lens capture detailed information about the user’s face, representing the training data that serves as the foundation for generating a lifelike avatar. The Vision Pro then leverages GANs to manipulate this media to create realistic but fabricated content.
However, this first generation of Personas has been met with criticism, with multiple users comparing the graphics to GTA San Andreas (released in 2004) or old video games. Nonetheless, the widespread application of digital avatars and deepfake technology is tangible - and they will only improve from here.
As deepfakes spread, we must respond with vigilance, ethics, and collective responsibility to mitigate potential harms. We felt a need to take a deeper dive into The Hong Kong case, as it provides a stark warning - unchecked, deepfakes could undermine truth and trust on an immense scale.
👑 Related Coverage 👑
Curated Updates
Here are the rest of the updates hitting our radar.
Machine Models
⭐ Eltropy Doubles Down on Fintech and Gen AI Integrations - Fintech Finance News
AI Applications in Finance
⭐ 56% of Canadian Financial Advisors Adopting Generative AI - Finance Magnates
Finally, an Accounting and Finance Automation AI Software, Secures $10M - Tech Crunch
Infrastructure & Middleware
⭐ AI in Fintech Market to Reach $49.43B by 2028 - Globe News Wire
Entrust: Betting Big on AI-Powered ID Verification - Fintech Magazine
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