8,572 research outputs found
DeepSign: Deep On-Line Signature Verification
Deep learning has become a breathtaking technology in the last years,
overcoming traditional handcrafted approaches and even humans for many
different tasks. However, in some tasks, such as the verification of
handwritten signatures, the amount of publicly available data is scarce, what
makes difficult to test the real limits of deep learning. In addition to the
lack of public data, it is not easy to evaluate the improvements of novel
proposed approaches as different databases and experimental protocols are
usually considered.
The main contributions of this study are: i) we provide an in-depth analysis
of state-of-the-art deep learning approaches for on-line signature
verification, ii) we present and describe the new DeepSignDB on-line
handwritten signature biometric public database, iii) we propose a standard
experimental protocol and benchmark to be used for the research community in
order to perform a fair comparison of novel approaches with the state of the
art, and iv) we adapt and evaluate our recent deep learning approach named
Time-Aligned Recurrent Neural Networks (TA-RNNs) for the task of on-line
handwritten signature verification. This approach combines the potential of
Dynamic Time Warping and Recurrent Neural Networks to train more robust systems
against forgeries. Our proposed TA-RNN system outperforms the state of the art,
achieving results even below 2.0% EER when considering skilled forgery
impostors and just one training signature per user
SynSig2Vec: Learning Representations from Synthetic Dynamic Signatures for Real-world Verification
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.Comment: To appear in AAAI 202
A Taxonomy of Deep Convolutional Neural Nets for Computer Vision
Traditional architectures for solving computer vision problems and the degree
of success they enjoyed have been heavily reliant on hand-crafted features.
However, of late, deep learning techniques have offered a compelling
alternative -- that of automatically learning problem-specific features. With
this new paradigm, every problem in computer vision is now being re-examined
from a deep learning perspective. Therefore, it has become important to
understand what kind of deep networks are suitable for a given problem.
Although general surveys of this fast-moving paradigm (i.e. deep-networks)
exist, a survey specific to computer vision is missing. We specifically
consider one form of deep networks widely used in computer vision -
convolutional neural networks (CNNs). We start with "AlexNet" as our base CNN
and then examine the broad variations proposed over time to suit different
applications. We hope that our recipe-style survey will serve as a guide,
particularly for novice practitioners intending to use deep-learning techniques
for computer vision.Comment: Published in Frontiers in Robotics and AI (http://goo.gl/6691Bm
Strengthening the security of cognitive packet networks
Route selection in cognitive packet networks (CPNs) occurs continuously for active flows and is driven by the users' choice of a quality of service (QoS) goal. Because routing occurs concurrently to packet forwarding, CPN flows are able to better deal with unexpected variations in network status, while still achieving the desired QoS. Random neural networks (RNNs) play a key role in CPN routing and are responsible to the next-hop decision making of CPN packets. By using reinforcement learning, RNNs' weights are continuously updated based on expected QoS goals and information that is collected by packets as they travel on the network experiencing the current network conditions. CPN's QoS performance had been extensively investigated for a variety of operating conditions. Its dynamic and self-adaptive properties make them suitable for withstanding availability attacks, such as those caused by worm propagation and denial-of-service attacks. However, security weaknesses related to confidentiality and integrity attacks have not been previously examined. Here, we look at related network security threats and propose mechanisms that could enhance the resilience of CPN to confidentiality, integrity and availability attacks
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