Advances in computer vision and pattern recognition 

About: Advances in computer vision and pattern recognition is an academic journal published by Springer International Publishing. The journal publishes majorly in the area(s): Computer science & Presentation (obstetrics). It has an ISSN identifier of 2191-6586. Over the lifetime, 9 publications have been published receiving 78 citations. The journal is also known as: Advances in pattern recognition (Internet).

DeepFakes Detection Based on Heart Rate Estimation: Single- and Multi-frame

Abstract: Abstract This chapter describes a DeepFake detection framework based on physiological measurement. In particular, we consider information related to the heart rate using remote photoplethysmography (rPPG). rPPG methods analyze video sequences looking for subtle color changes in the human skin, revealing the presence of human blood under the tissues. This chapter explores to what extent rPPG is useful for the detection of DeepFake videos. We analyze the recent fake detector named DeepFakesON-Phys that is based on a Convolutional Attention Network (CAN), which extracts spatial and temporal information from video frames, analyzing and combining both sources to better detect fake videos. DeepFakesON-Phys has been experimentally evaluated using the latest public databases in the field: Celeb-DF v2 and DFDC. The results achieved for DeepFake detection based on a single frame are over 98% AUC (Area Under the Curve) on both databases, proving the success of fake detectors based on physiological measurement to detect the latest DeepFake videos. In this chapter, we also propose and study heuristical and statistical approaches for performing continuous DeepFake detection by combining scores from consecutive frames with low latency and high accuracy (100% on the Celeb-DF v2 evaluation dataset). We show that combining scores extracted from short-time video sequences can improve the discrimination power of DeepFakesON-Phys.

Detection of AI-Generated Synthetic Faces

Abstract: Abstract In recent years there have been astonishing advances in AI-based synthetic media generation. Thanks to deep learning methods it is now possible to generate visual data with a high level of realism. This is especially true for human faces. Advanced deep learning tools allow one to easily change some specific attributes of a real face or even create brand new identities. Although this opens up a large number of new opportunities, just think of the entertainment industry, it also undermines the trustworthiness of media content and supports the spread of fake identities over the internet. In this context, there is a fundamental need to develop robust and automatic tools capable of distinguishing synthetic faces from real ones. The scientific community is making a huge research effort in this field, proposing several interesting approaches. However, a universal detector is yet to come. Fundamentally, the research in this field is like a cat and mouse game, with new detectors that are designed to deal with powerful synthetic face generators, while the latter keep improving to produce more and more realistic images. In this chapter we will present the most effective techniques proposed in the literature for the detection of synthetic faces. We will analyze their rationale, present real-world application scenarios , and compare different approaches in terms of accuracy and generalization ability.

Face Morphing Attack Detection Methods

Abstract: Abstract Morphing attacks pose a serious threat to face recognition systems, especially in the border control scenario. In order to guarantee a secure operation of face recognition algorithms in the future, it is necessary to be able to reliably detect morphed facial images and thus be able to reject them during enrolment or verification. This chapter provides an overview of morphing attack detection algorithms and metrics to measure and compare their performance. Different concepts of morphing attack detection are introduced and state-of-the-art detection methods are evaluated in a comprehensive cross-database experiments considering various realistic image post-processings.