Core Viewpoint - The article discusses the rise of fraudulent practices in e-commerce, where buyers use AI-generated images to falsely claim product defects in order to obtain refunds, highlighting a growing trust crisis between consumers and sellers [1][9][24]. Group 1: Fraudulent Practices - Some buyers are using AI to create fake defect images of products, such as making a good durian appear rotten, to exploit sellers for refunds [1][8]. - This practice has evolved from earlier methods where buyers used basic photo editing tools, but AI-generated images are now much harder to detect [8][9]. - Sellers face challenges in verifying claims due to the nature of certain products, like fruits, which are difficult to return [1][6]. Group 2: Seller Responses - Many sellers opt to issue refunds or partial compensation rather than deal with the complexities of returns, especially for low-cost items [6][9]. - Sellers have attempted to mitigate losses by requiring buyers to destroy the claimed defective items, but this has also been circumvented by AI [6][11]. Group 3: Proposed Solutions - Suggestions to combat this issue include requiring buyers to submit videos of the defective items, but the effectiveness of this method is uncertain due to advancements in AI [15][18]. - Other proposals involve capturing multiple angles of the product to exploit AI's weaknesses, but these are seen as temporary fixes [16][18]. - A more robust solution could involve creating a comprehensive evidence chain that includes detailed video documentation of the defect [18]. Group 4: Technological Solutions - The introduction of digital watermarking and content provenance technologies, such as C2PA and Google's SynthID, could help in tracing and verifying AI-generated content [20][24]. - These technologies aim to embed invisible digital identifiers in AI-generated media, making it easier to track and authenticate content [22][24]. - The ongoing evolution of AI detection technologies is crucial in the ongoing battle against fraudulent practices, creating a continuous cycle of adaptation between fraudsters and sellers [24][25]. Group 5: Industry Implications - The rapid development of AI technologies has lowered the barriers for both buyers and sellers to engage in deceptive practices, leading to increased costs for both parties in terms of trust and verification [22][24]. - E-commerce platforms are exploring various strategies, including enhancing evidence integrity and implementing stricter user behavior monitoring to combat fraud [24][25]. - Establishing a unified, traceable digital content standard is seen as essential for resolving the current trust crisis in the industry [24][25].

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].