Improving Signature Forgery Analysis through Deep Learning Classification Algorithms

Resampling Forgery Detection Using Deep Learning and A-Contrario Analysis

The amount of digital imagery recorded has recently grown exponentially, and with the advancement of software, such as Photoshop or Gimp, it has become easier to manipulate images. However, most images on the internet have not been manipulated and any automated manipulation detection algorithm must carefully control the false alarm rate. In this paper we discuss a method to automatically detect...

Statistical Machine Learning Algorithms for Target Classification from Acoustic Signature

Machine learning classification algorithms are relevant to a large number of Army classification problems, including the determination of a weapon class from a detonation acoustic signature. However, much such work has been focused on classification of events from small weapons used for asymmetric warfare, which have been of importance in recent years. In this work we consider classification of...

Recent Results on Signature Forgery

A weakness has been found in the ISO 9796 signature standard using RSA. The padding method proposed by the standard is the origin of the problem and will require probable changes in ISO 9796 parts 1 and 2. This weakness was discovered by Gemplus’ cryptography division and the ENS and UCL cryptography groups while implementing the standard. A paper reporting the problem will be presented at Cryp...

Improving classification Accuracy of Neural Network through Clustering Algorithms

A common problem in bio medical data using neural networks for classification purposes are complex nature of the data, high dimensionality, convoluted and overlapping classes with their remarkable ability to derive meaning from complicated data, can be extract patterns and trends are too complex. Bio medical classification is a complex process to make decisions. Classification performance of th...

Forgery Above a Genuine Signature