AI Safeguards, Generative AI and the Pandora Box: AI Safety Measures to Protect Businesses and Personal Reputation
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 6 of its 17 references that are in the map. First posted on arXiv 8 Jan 2026.
Builds on
6 of 17 references.
References · 6
- 2016WaveNet: A Generative Model for Raw Audiocited by 1,710
- 2018An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modelingcited by 1,447
- 2019FaceForensics++: Learning to Detect Manipulated Facial Imagescited by 1,095
- 2019InceptionTime: Finding AlexNet for Time Series Classificationcited by 386
- 2021Spatiotemporal Inconsistency Learning for DeepFake Video Detectioncited by 88
- 2018Temporal Convolutional Neural Network for the Classification of Satellite Image Time Seriescited by 76
Built on by
Not cited yet.