伴随着生成式人工智能(GAI)的爆发式发展，变化正悄悄渗入全球学术体系的每个角落，从留学申请的 个人文书再到国内外课堂上的学术论文，ChatGPT、DeepSeek等工具正悄然嵌入学生的创作过程，成为 他们的"学术助手"。 这种新力量既为学习带来了前所未有的效率，也引发了各大高校对于学术诚信的深切忧虑。校方正在加 速升级反制措施，日益精湛的检测技术成为AI"攻防战"的核心武器。 禁止AI写作 2022年11月，ChatGPT一经推出便迅速在社交媒体上走红，不到一周用户数便突破100万，月访问量达 到2100万次。两个月后的2023年1月底，其月活跃用户就已突破1亿，成为史上用户增长最快的消费级应 用程序。 在美国，宾夕法尼亚大学沃顿商学院、哥伦比亚大学、布朗大学等15所Top30高校已明确将AI代写申请 文书认定为学术欺骗，并规定违者将被取消申请资格。与此相比，耶鲁大学的态度相对开放，鼓励学生 将AI作为完善想法的辅助工具，但前提是保持学术诚信。招生办公室建议将AI用于头脑风暴，而非生 成过于精致的成品内容，以免引发对申请者真实性的质疑。纽约市教育部发言人詹娜.莱尔(Jenna Lyle) 则指出，部分限制措 ...

Core Viewpoint - The article discusses the increasing misuse of AI technology by both buyers and sellers in e-commerce, leading to a trust crisis and the need for better verification methods to combat fraud [2][10][21]. Group 1: Buyer Misuse of AI - Some buyers are using AI-generated images to falsely claim product defects in order to obtain refunds, exploiting the difficulty of verifying the condition of perishable goods like fruits [2][6]. - This practice has evolved from earlier methods where buyers used basic photo editing tools, making it harder for sellers to detect fraud due to the sophistication of AI-generated images [8][10]. - The phenomenon reflects a "tit-for-tat" mentality among buyers who have previously been deceived by sellers using AI-enhanced product images [10][21]. Group 2: Seller Misuse of AI - Sellers are also misusing AI to create misleading product images, over-enhancing ordinary items, and generating fake reviews, which contributes to the issue of "goods not matching the description" [10][24]. - The article highlights that sellers may use virtual models and AI-generated content to cut costs, further complicating the authenticity of product representations [10][24]. Group 3: Proposed Solutions - Various proposed solutions to combat this issue include requiring buyers to submit videos of defective products, taking multiple photos from different angles, and using in-app cameras to prevent the upload of AI-generated images [11][15][24]. - However, these solutions have limitations, as advanced AI tools can still generate convincing content, making it challenging to establish foolproof verification methods [11][15][23]. Group 4: Technological Innovations - The article suggests that implementing digital watermarking and content provenance technologies could help in identifying and tracing AI-generated content, thus enhancing trust in e-commerce [19][21]. - The development of standards like C2PA and tools such as Google's SynthID aims to embed invisible watermarks in AI-generated media, which could serve as a digital identity for content [19][21][26]. Group 5: Ongoing Challenges - The ongoing "cat-and-mouse" game between AI generation and detection technologies poses a continuous challenge, as both sides evolve rapidly [23][24]. - E-commerce platforms are exploring various strategies, including strengthening evidence chains and utilizing big data analytics to monitor user behavior and detect anomalies [24][26].

《滕王阁序》是AI生成的？近日，AI检测工具屡屡爆出"翻车"的新闻，如《荷塘月色》被标 注"62.88%AI率"，《三体》片段被标红警示，引发公众对AI检测工具科学性的热议。 为探究AI检测工具的识别能力与技术原理，南方都市报、南都大数据研究院选取了国内10款热门的文 本、图片AIGC检测工具进行了抽样测评。结果显示：文本检测工具中，检测标准参差不齐，明显误 判、漏检、乱检的情况均有存在。而图片检测工具中，均对PS后的摄影图片难以识别。 当AI检测被用于高校毕业的"门槛"、期刊评审的"硬指标"，一度引发了困惑质疑。在专家看来，当前AI 检测技术尚处于探索阶段，误判或是技术演进的必经过程，强行将不稳定的技术跟学术诚信关联不可 取。但长远来看，技术迭代与合规框架构建仍需"双轨并行"。 AI文本检测 存误判、漏检、乱检"难题" AI检测工具靠谱吗？南都大数据研究院本次测评样本为知网、PaperPass、万方、维普、朱雀大模型检 测、挖错网、大雅、PaperYY、团象、茅茅虫共10款国内热门的文本、图片AIGC检测工具。 首先是文本类检测，尝试使用四类文章来测试对真实文章、以及不同程度AI生成内容的识别率。四篇 文章 ...

Core Viewpoint - The increasing reliance on AI detection systems for academic integrity is leading to significant misjudgments, causing concern among students regarding the originality of their work [1][2][3] Group 1: AI Detection Issues - Many students report high AI detection rates in their theses, with some reaching as high as 90%, raising fears of misjudgment [1] - Classic literary works have also been flagged by AI detection systems, with instances of 100% AI generation likelihood, indicating a flaw in the technology [1] - The current AI detection methods, based on machine learning and natural language processing, have limitations that lead to frequent misjudgments [2] Group 2: Limitations of AI Detection Technology - The variability of language makes it difficult for AI systems to accurately determine the source of text, resulting in misjudgments [2] - As generative AI improves, its outputs increasingly resemble human writing, complicating the detection process [2] - Inadequate training data and simplified detection processes for efficiency can further reduce the accuracy of AI detection systems [2] Group 3: Recommendations for Improvement - There is an urgent need to enhance the accuracy and credibility of AI detection technologies through improved standards and regulations [3] - Establishing a standardized system that covers technical principles, data application, and result evaluation is essential for reliable detection [3] - Implementing regular audits by third-party organizations can ensure the transparency and accuracy of detection algorithms, preventing conflicts of interest [3]