AI-Forged Damage Photos Are Breaking Ecommerce Refunds

Scammers are using AI-generated images and videos to fake product damage and obtain refunds, with cases in China highlighting how easy tools and overwhelmed reviewers enable abuse. The trend is global and includes organized operations that mass-file claims, while merchants’ AI-based detection remains unreliable. Ecommerce platforms will need stronger verification and policy changes to protect trust without punishing legitimate shoppers.

Key Points

AI-generated images are increasingly used to fake product damage and secure refunds, especially in categories where returns aren’t required (groceries, budget beauty, fragile goods).
A prominent Douyin case exposed fabricated crab “damage” videos through biological inconsistencies, prompting police action and buyer detention.
Forter reports a global rise of 15%+ in AI-doctored refund images, with organized crime scaling attacks via mass submissions and rotating IPs.
Sellers are turning to AI to detect manipulation, but current detection and platform adjudication are unreliable.
Ecommerce’s trust foundation is weakening; watermarks are insufficient, and platforms must implement new verification, policy updates, and accountability for AI-enabled scams.

Sentiment

Overall, the sentiment is mixed, leaning towards concerned and pessimistic. While there's a strong debate on the novelty and verifiable scale of AI-driven refund fraud, a significant portion of the discussion acknowledges the problem's existence and potential for widespread societal impact, often expressing concern or pessimism about easy solutions and the broader erosion of trust in digital information. Some participants remain skeptical about the evidence, while others are critical of the skepticism itself.

In Agreement

Generative AI is a new, scalable vector for fraud, making it easier and cheaper than before, significantly exacerbating existing problems.
The problem extends beyond refunds to other areas like education (homework fraud), remote interviewing, and general information veracity, leading to widespread societal distrust and 'information overload'.
Skepticism about the problem's scale is often unwarranted; it is happening and will likely grow, following a pattern of AI's malicious uses becoming widely recognized over time.
Traditional fraud detection methods (e.g., flagging users) are insufficient or easily bypassed by organized groups.
Some form of 'no-AI proof' or verifiable image authentication is theoretically needed, although technically challenging to implement effectively.
The ease of content creation by AI will overwhelm information systems, effectively controlling narratives by sandblasting human attention and making verification too costly.

Opposed

Refund fraud is not new; scammers have always existed, and AI merely automates or enhances existing types of fraud rather than creating entirely new problems.
The scale of the AI-driven refund problem might be exaggerated or hyped, with a lack of independently verifiable data (e.g., court cases, specific merchant data) to support widespread claims.
Simple solutions like requiring item returns could fix the issue, as is common in physical retail and already implemented in some online contexts (e.g., EU law).
Technical solutions for 'no-AI proofs' (e.g., camera signatures, 3D scans) are likely infeasible or easily circumvented due to the 'analog loophole' (photographing an AI image on a screen) or other forms of manipulation.
The impact of AI on other areas like education might be overblown, or even lead to positive changes in teaching and grading methods by forcing a re-evaluation of current systems.
Proposed technical solutions could lead to privacy invasions or overly restrictive, DRM-like systems on personal devices.