When the Codec Hallucinates: User Perceptions of Miscompressed Images

People exchange images every day. New methods for image compression leverage neural networks to save bandwidth, but they can undermine the semantic integrity. The term miscompression refers to unintended semantic changes of image details, introduced by generative AI during neural (de)compression. Although prior work has speculated about the resulting risks, no empirical evidence exists on how people perceive these novel compression artifacts. In this study, 115 human subjects compared original images with conventionally compressed, neurally compressed, and miscompressed images. Participants perceive that miscompressions elevate the risk of misunderstandings when communicating with images. They also frequently attribute miscompressions to intentional editing, whereas conventional JPEG artifacts are more often recognized as distortions. This paper proposes a method to study this new phenomenon, provides the first empirical evidence of user perceptions of miscompressions, and derives implications for trust in images, as well as interface designs that mitigate the risk.