用时间一致性学习检测生成式AI伪造内容，保护企业和个人声誉。
AI Safeguards, Generative AI and the Pandora Box: AI Safety Measures to Protect Businesses and Personal Reputation
- 引入时间一致性学习（TCL）提升深度伪造检测能力
- 基于预训练时序卷积网络，在5类伪造问题上准确率显著提升
- 适合关注AI安全与风险防控的从业者和研究者
生成式AI虽推动了内容创作革新，却也无意打开了深度伪造带来的社会隐患之潘多拉魔盒，对企业和个人声誉造成威胁。本文研究生成式AI在各行业的应用及其影响，提出基于神经网络的混合检测技术以识别伪造内容。重点聚焦于通过时间一致性学习（Temporal Consistency Learning, TCL）构建高效检测机制。利用预训练时序卷积网络（TCN）模型进行训练与性能对比，结果表明该方法在五类典型伪造问题上优于现有技术，显著提升了检测准确率。研究强调主动识别的重要性，为降低生成式AI潜在风险提供了有效路径。
原文摘要 · Abstract (English)
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.
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