Handwritten Signature Generation Using Denoising Diffusion Probabilistic Models with Auxiliary Classification Processes

Automatic signature verification has been widely studied for authentication purposes in real life, but limited data availability still poses a significant challenge. To address this issue, we propose a method with a denoising diffusion probabilistic model (DDPM) to generate artificial signatures that closely resemble authentic ones. In the proposed method, we modified the noise prediction process of the DDPM to allow the generation of signatures specific to certain classes. We also employed an auxiliary classification process to ensure that the generated signatures closely resemble the originals. The model was trained and evaluated using the CEDAR signature dataset, a widely used collection of offline handwritten signatures for signature verification research. The results indicate that the generated signatures exhibited a high similarity to the originals, with an average structural similarity index (SSIM) of 0.9806 and a root mean square error (RMSE) of 0.1819. Furthermore, when the generated signatures were added to the training data and the signature verification model was retrained and validated, the model achieved an accuracy of 94.87% on the test data, representing an improvement of 0.061 percentage points compared to training on only the original dataset. These results indicate that the generated signatures reflect the diversity that original signatures may exhibit and that the generated data can enhance the performance of verification systems. The proposed method introduces a novel approach to utilizing DDPM for signature data generation and demonstrates that the auxiliary classification process can reduce the likelihood of generated data being mistaken for forged signatures.