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Industry experts highlight AI detection tools amid deepfake surge

During the General Elections of 2024, social media platforms were flooded with deepfake videos featuring prominent actors like Aamir Khan and Ranveer Singh endorsing various political parties, all thanks to AI voice swap technology.

News Arena Network - New Delhi - UPDATED: May 14, 2024, 08:06 AM - 2 mins read

As deepfakes gain traction during the Lok Sabha elections, understanding the mechanisms behind AI detection tools becomes crucial.

Industry experts highlight AI detection tools amid deepfake surge

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As deepfakes gain traction during the Lok Sabha elections, understanding the mechanisms behind AI detection tools becomes crucial.

 

Despite their imperfections and occasional misfires, these tools are indispensable in today's AI-driven landscape, as highlighted by industry experts.

 

During the General Elections of 2024, social media platforms were flooded with deepfake videos featuring prominent actors like Aamir Khan and Ranveer Singh endorsing various political parties, all thanks to AI voice swap technology.

 

While these AI-generated content (AIGC) posts managed to sway many, a significant portion of voters turned to fact-check stories for verification, often citing the use of AI detection tools.

 

But how do these AI content detectors operate? According to Jon Gillham, founder of AI text detector Originality.AI, these detectors rely on supervised learning from vast datasets containing both AI-generated and human-authored content.

 

By discerning patterns between the two, these algorithms can effectively flag AI-generated material.

 

Digvijay Singh, founder of contrails.ai, delineated two types of AI-generated content: completely AI-generated and AI-modified.

 

Detection tools employ discriminative AI methodologies, utilizing appropriately trained models on meticulously curated datasets to classify content as either real or AI-generated.

 

Ben Colman, CEO of Reality Defender, elucidated their approach, employing convolutional neural networks trained on diverse datasets to ensure unbiased detection across various demographics.

 

Gillham's Originality.AI was conceived to combat plagiarism from AI content generators, while Colman's Reality Defender initially operated as a non-profit before scaling up to preemptively tackle emerging challenges.

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