AI 中文总结
该研究针对数字绘画作者身份验证的挑战，提出IDraw框架，构建首个多模态数据集，可从完整图像推断绘画行为、抑制内容干扰，在9种主干网络下将验证误差最多降低40%。
AI 中文摘要
随着数字绘画在网络上的分享日益增多，可靠的作者身份验证对于保护艺术家权益、解决相关纠纷变得愈发重要。然而当作者身份受到质疑时，验证往往只能依赖存在争议的绘画作品，以及已知为被主张艺术家创作的参考绘画。该场景存在两大挑战：其一，艺术家特有的绘画行为（如压感、移动速度）具有辨识度，但完整的绘画作品无法提供这类信息；其二，所描绘的对象或场景的相似性，会掩盖源于艺术家本人的相似性。我们提出IDraw框架，该框架从绘画作品与训练艺术家的数位笔传感器信号中学习，能够在后续作者身份争议场景下，从完整图像中推断绘画行为，无需待验证艺术家的传感器数据。IDraw还通过识别不同艺术家绘制同一对象的绘画所共有的信息并在对比前进行抑制，来降低绘画内容的影响。为支撑该方法，我们构建了首个用于数字绘画作者身份验证的多模态数据集，包含37位艺术家的1110幅绘画作品，以及14种数位笔传感器信号。在9种图像编码器主干网络、针对未见艺术家的评估中，IDraw的表现始终优于基于图像的标准验证方法，将验证误差最多降低40%。这些结果表明，从完整图像推断绘画行为并抑制绘画内容，可提升数字绘画作者身份验证的效果。
英文摘要
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.
