Network radar countermeasure systems verification#

Further comprehensive conditions were investigated in 2019 to account for all three attacks considered in previous challenges, ushering in the development of an extensive dataset using state-of-the-art voice conversion and speech synthesis systems. In order to be able to test the performance of countermeasure systems in real conditions, the organizers produced the dataset in different environmental conditions and using different devices. The second ASVspoof challenge held in 2017, focused more on the replay attack (also called physical access scenario). A variety of methods and systems were proposed and implemented by ASV organizers to produce spoof samples, exciting the interest of many researchers intrigued by both the challenge and the dataset provided therein.

In consequence, since 2015, ASVspoof challenges were held for research communities worldwide to try and enhance ASV systems so as to make them robust against spoofing attacks.Ī total of four ASVspoof challenges have thus far been held, with the first instance in 2015, covering only speech synthesis and voice conversion (also called logical access scenario) attacks. The threats facing ASV systems in terms of spoof attacks are potentially high and may amass to serious implications. In particular, ASV systems are also vulnerable in the face of four types of attacks, including recording and replaying the voice of the authorized person (replay attack), text-to-speech systems that are trained with the voice of the targeted person, voice conversion systems, and speaker imitation. Likewise, a fingerprint system can be spoofed by copying a fingerprint. A system based on face recognition for example, may be spoofed by simply displaying a person’s image (photograph) to the system. Nevertheless, virtually all of these systems are vulnerable to spoof attacks (i.e., spoofable).

Network radar countermeasure systems verification#

Along this thread, Automatic Speaker Verification (ASV) system and other biometric systems such as face recognition, electronic signatures, iris-based, and hybrid methods have been proposed as a means to satisfy user needs. Such circumstances call for an easy-to-use, accurate, and efficient authentication system. Remote authentication has excited great interests in various academic circles and otherwise, given the increasing reliance on online applications as well as the onset of certain conditions such as the COVID-19 pandemic. Furthermore, using the LFCC feature, and the SE-Res2Net50 for the perception branch, the proposed model achieved EER = 1.89% and t-DCF = 0.507 in the Logical Access (LA) scenario, which to the best of our knowledge, is the best single system ASV spoofing countermeasure method. The proposed method on ASVspoof 2019 dataset achieved EER = 0.86% and t-DCF = 0.0239 in the Physical Access (PA) scenario using the logPowSpec as the input feature extraction method. The new EfficientNet-A0 architecture was optimized and employed for the perception branch, with nearly ten times fewer parameters and approximately seven times fewer floating-point operations than the SE-Res2Net50 as the best existing network. The perception branch, is used for our main purpose which is spoof detection. The attention branch provides an attention mask that improves the classification performance and at the same time is interpretable from a human point of view. The EABN is based on attention and perception branches. Accordingly, the present study proposes the Efficient Attention Branch Network (EABN) architecture with a combined loss function to address the model generalization to unseen attacks. A joint improvement of components of ASV spoof detection systems including the classifier, feature extraction phase, and model loss function may lead to a better detection of attacks by these systems. As evidenced by the latest ASVspoof 2019 countermeasure challenge, models currently deployed for the task of ASV are, at their best, devoid of suitable degrees of generalization to unseen attacks. Many endeavors have sought to develop countermeasure techniques as enhancements on Automatic Speaker Verification (ASV) systems, in order to make them more robust against spoof attacks.