AI Safeguards, Generative AI and the Pandora Box: AI Safety Measures to Protect Businesses and Personal Reputation

TL;DR

This paper discusses AI safety measures for generative AI, focusing on detection techniques like Temporal Consistency Learning to identify deepfakes and mitigate risks to businesses and individuals.

Contribution

It introduces a novel Temporal Consistency Learning method using pretrained TCNs that outperforms existing approaches in detecting AI-generated dark side content.

Findings

TCN models outperform other detection methods

Achieved high accuracy in identifying deepfake content

Proactive detection reduces AI-related social hazards

Abstract

Generative AI has unleashed the power of content generation and it has also unwittingly opened the pandora box of realistic deepfake causing a number of social hazards and harm to businesses and personal reputation. The investigation & ramification of Generative AI technology across industries, the resolution & hybridization detection techniques using neural networks allows flagging of the content. Good detection techniques & flagging allow AI safety - this is the main focus of this paper. The research provides a significant method for efficiently detecting dark side problems by imposing a Temporal Consistency Learning (TCL) technique. Through pretrained Temporal Convolutional Networks (TCNs) model training and performance comparison, this paper showcases that TCN models outperforms the other approaches and achieves significant accuracy for five dark side problems. Findings highlight how important it is to take proactive measures in identification to reduce any potential risks associated with generative artificial intelligence.