Deep Generative Attacks and Countermeasures for Data-Driven Offline
  Signature Verification
An Ngo MinhPhuong Cao Rajesh Kumar
While previous studies have explored attacks via random, simple, and skilled
forgeries, generative attacks have received limited attention in the
data-driven signature verification (DASV) process. Thus, this paper explores
the impact of generative attacks on DASV and proposes practical and
interpretable countermeasures. We investigate the power of two prominent Deep
Generative Models (DGMs), Variational Auto-encoders (VAE) and Conditional
Generative Adversarial Networks (CGAN), on their ability to generate signatures
that would successfully deceive DASV. Additionally, we evaluate the quality of
generated images using the Structural Similarity Index measure (SSIM) and use
the same to explain the attack's success. Finally, we propose countermeasures
that effectively reduce the impact of deep generative attacks on DASV.
  We first generated six synthetic datasets from three benchmark
offline-signature datasets viz. CEDAR, BHSig260- Bengali, and BHSig260-Hindi
using VAE and CGAN. Then, we built baseline DASVs using Xception, ResNet152V2,
and DenseNet201. These DASVs achieved average (over the three datasets) False
Accept Rates (FARs) of 2.55%, 3.17%, and 1.06%, respectively. Then, we attacked
these baselines using the synthetic datasets. The VAE-generated signatures
increased average FARs to 10.4%, 10.1%, and 7.5%, while CGAN-generated
signatures to 32.5%, 30%, and 26.1%. The variation in the effectiveness of
attack for VAE and CGAN was investigated further and explained by a strong (rho
= -0.86) negative correlation between FARs and SSIMs. We created another set of
synthetic datasets and used the same to retrain the DASVs. The retained
baseline showed significant robustness to random, skilled, and generative
attacks as the FARs shrank to less than 1% on average. The findings underscore
the importance of studying generative attacks and potential countermeasures for
DASV.