{
  "annotation_id": "9e7fa5b4-2a2a-4367-b26d-9fa948b66535",
  "date_created": "2026-02-17T05:53:08.755000Z",
  "date_modified": "2026-02-17T05:53:08.755000Z",
  "file_hash": "658b48c5e31ebfbf44987a9e9d2add1dee388de08b2123b4878ba6e5befd9325",
  "private": false,
  "record": {
    "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 \u0026 ramification of Generative AI technology across industries, the resolution \u0026 hybridization detection techniques using neural networks allows flagging of the content. Good detection techniques \u0026 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.",
    "arxiv_id": "2601.06197",
    "authors": [
      "Prasanna Kumar"
    ],
    "categories": [
      "cs.AI",
      "cs.CR"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "title": "AI Safeguards, Generative AI and the Pandora Box: AI Safety Measures to Protect Businesses and Personal Reputation",
    "url": "https://arxiv.org/abs/2601.06197",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "db360161-aa7e-45d3-84e7-a2580f535d10",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}