  An open research problem in automatic signature verification is the skilled
forgery attacks. However, the skilled forgeries are very difficult to acquire
for representation learning. To tackle this issue, this paper proposes to learn
dynamic signature representations through ranking synthesized signatures.
First, a neuromotor inspired signature synthesis method is proposed to
synthesize signatures with different distortion levels for any template
signature. Then, given the templates, we construct a lightweight
one-dimensional convolutional network to learn to rank the synthesized samples,
and directly optimize the average precision of the ranking to exploit relative
and fine-grained signature similarities. Finally, after training, fixed-length
representations can be extracted from dynamic signatures of variable lengths
for verification. One highlight of our method is that it requires neither
skilled nor random forgeries for training, yet it surpasses the
state-of-the-art by a large margin on two public benchmarks.
