IDraw: Artist Verification from Digital Drawing Images
2026-08-03 • Computer Vision and Pattern Recognition
Computer Vision and Pattern Recognition
AI summaryⓘ
The authors address the problem of verifying who made a digital drawing when only finished images are available. They created a system called IDraw that learns how people draw by using sensor data from tablets, and then can guess drawing styles from just images later. Their method also tries to ignore what the drawing shows, focusing instead on how it was made, which helps tell artists apart. They tested IDraw on a new dataset with many drawings and sensors, and it did better than usual image-based checks, making fewer mistakes.
digital drawingauthorship verificationtablet-pen sensorsdrawing behaviorimage encodingmultimodal datasetpen pressuremovement speedimage-based verification
Authors
Nayoung Kim, Nan Jiang, Bangjie Sun, Jaewon Shin, Sojeong Kim, Jun Han
Abstract
As digital drawings are increasingly shared online, reliable authorship verification has become important for protecting artists and resolving disputes. Yet when authorship is questioned, verification may have to rely only on the disputed drawing and reference drawings known to be created by the claimed artist. This setting is challenging for two reasons. First, artist-specific drawing behavior, such as pen pressure and movement speed, is informative but is not available from a completed drawing. Second, similarities in the depicted object or scene can obscure similarities arising from the artist. We propose IDraw, a framework that learns from drawings paired with tablet-pen sensor signals collected from separate training artists. This allows IDraw to infer drawing behavior from completed images during a later authorship dispute, without requiring sensor data from the artist being verified. IDraw also reduces the influence of drawing content by identifying information shared by drawings of the same object across different artists and suppressing it before comparing drawings. To support this approach, we construct the first multimodal dataset for digital drawing authorship verification, containing 1,110 drawings from 37 artists and 14 types of tablet-pen sensor signals. Evaluated on previously unseen artists across nine image-encoder backbones, IDraw consistently outperforms standard image-based verification and reduces verification error by up to 40%. These results demonstrate that inferring drawing behavior from completed images and suppressing drawing content improve digital drawing authorship verification.