Special Issue on “Adversarial Deep Learning in Biometrics & Forensics”
• 大类 : 工程技术 - 3区
• 小类 : 计算机：人工智能 - 3区
• 小类 : 工程：电子与电气 - 3区
In the short course of a few years, deep learning has changed the rules of the game in a wide array of scientific disciplines, achieving state-of-the-art performance in major pattern recognition application areas. Notably, it has been used recently even in fields like image biometrics and forensics (e.g. face recognition, forgery detection and localization, source camera identification, etc).
However, recent studies have shown their vulnerability to adversarial attacks: a trained model can be easily deceived by introducing a barely noticeable perturbation in the input image. Such a weakness is obviously more critical for security-related applications calling for possible countermeasures. Indeed, adversarial deep learning will create high impact in the field of Biometrics and Forensics in the near future.
The aim of this special issue is hence to gather innovative contributions on methods able to resist adversarial attacks on deep neural networks applied both in image biometrics and forensics. Therefore, it will encourage proposals of novel approaches and more robust solutions.
Submissions are encouraged, but not limited, to the following topics:
Adversarial biometric recognition
Attack transferability in biometric applications
Physical attacks in biometric authentication systems
Attacks to person re-identification systems
Poisoned enrollment datasets
Multimodal biometric systems as a defense
Blind defense at test time for forensic and biometric systems
Novel counter-forensics methods
Design of robust forgery detectors
Adversarial patches in forensic applications
Adversarial attack and defense in video forensics
Steganography and steganalysis in adversarial settings