“数据投毒”或诱发有害输出！AI数据污染分为几类？专家解读→

Core Viewpoint - The national security department has issued a warning about "data poisoning" in AI, which can lead to harmful outputs due to the manipulation, fabrication, and repetition of data [1]. Group 1: Data Poisoning Overview - "Data poisoning" primarily targets two areas: visual recognition and natural language processing [3]. - An example of data poisoning involves altering training data, such as adding a green dot to a zebra image, which can mislead AI models during training [3]. - Even a few contaminated samples among thousands can significantly disrupt the AI model's ability to recognize similar objects correctly [3]. Group 2: Types of Data Pollution - There are two main types of AI data pollution: one involves malicious human intervention to mislead AI outputs, and the other involves the unfiltered inclusion of harmful information from vast internet data collections [5]. - If untrustworthy data is not identified and removed, it can compromise the reliability of AI outputs [5]. Group 3: Data Sources and Risks - AI models require extensive data for training, often sourced from the internet, including various forms of media [7]. - The potential for data contamination exists as anyone can contribute data online, which may lead to the AI model being influenced by unsafe or polluted data [7].