Helio Pedrini

TL;DR: This work assumes a very limited knowledge about biometric spoofing at the sensor to derive outstanding spoofing detection systems for iris, face, and fingerprint modalities based on two deep learning approaches based on convolutional networks.

TL;DR: In this paper, the authors proposed two deep learning approaches for spoofing detection of iris, face, and fingerprint modalities based on a very limited knowledge about biometric spoofing at the sensor.

TL;DR: This paper proposes a forgery detection method that exploits subtle inconsistencies in the color of the illumination of images that is applicable to images containing two or more people and requires no expert interaction for the tampering decision.

TL;DR: This competition is to compare the performance of different state-of-the-art algorithms on the same database using a unique evaluation method and the results suggest the investigation of more complex attacks.

TL;DR: This paper presents a novel strategy for extending the GLCM to multiple scales through two different approaches, a Gaussian scale-space representation, which is constructed by smoothing the image with larger and larger low-pass filters producing a set of smoothed versions of the original image, and an image pyramid,Which is defined by sampling the image both in space and scale.