Difference between revisions of "Multimedia Forensics"

Revision as of 13:46, 22 December 2022

Recent advances in machine learning have made it easier to create fake images and videos. Users and computing systems are facing increasing difficulties in differentiating forged contents from original ones. In this project, we focus on various aspects of detecting fake content, including detecting makeup attacks in biometrics systems to identifying forged videos and images.

People

Revealing True Identity: Detecting Makeup Attacks in Face-based Biometric Systems

Face-based authentication systems are among the most used biometric systems, because of the ease of capturing face images in a non-intrusive way. These systems are, however, susceptible to various attacks, including printed faces, artificial masks, and makeup attacks. Makeup attacks are the hardest to detect in biometric systems because makeup can substantially alter the facial features of a person, including making them appear older/younger, modifying the shape of their eyebrows/beard/mustache, and changing the color of their lips and cheeks (Fig. 1). Makeup can even trick human agents trying to identify a person standing before them.

We proposed a novel solution to detect makeup attacks. The idea of our approach is to design a generative adversarial network (Figure 2) for removing makeup from face images while retaining their essential facial features and then comparing the face images before and after removing makeup. Our approach allows biometric systems to efficiently detect various combinations of makeup, which can be numerous and changing with time. This is in contrast to prior approaches, which require substantial amounts of “labeled data” to train the biometric system for all possible makeup combinations. This is difficult to achieve in practice and thus makes current biometric systems vulnerable to sophisticated makeup attacks.

We collected a unique dataset of what we call malicious makeup, which is makeup purposely applied to deceive security systems, especially unattended ones where there are no humans to question the potentially weird looks of the makeup. Using extensive objective and subjective studies, we showed that our solution produces high detection accuracy and substantially outperforms the state-of-the-art.

This work was supported by IARPA through a sub-contract from the University of Southern California (USC), and it was done in collaboration with Dr. Wael Abd-Almageed and Dr. Mohamed Hussein of ISI at USC.

Code and Datasets

https://github.com/maarab-sfu/revealing-true-identity

Publications

M. Arab, P. Azadi Moghadam, M. Hussein, W. Abd-Almageed, and M. Hefeeda. Revealing True Identity: Detecting Makeup Attacks in Face-based Biometric Systems, In Proc. of ACM Multimedia Conference (MM'20), Seattle, WA, October 2020.

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 '''Revealing True Identity: Detecting Makeup Attacks in Face-based Biometric Systems'''
-Face-based authentication systems are among the most commonly used biometric systems, because of the ease of capturing face images at a distance and in non-intrusive way. These systems are, however, susceptible to various presentation attacks, including printed faces, artificial masks, and makeup attacks. In this paper, we propose a novel solution to address makeup attacks, which are the hardest to detect in such systems because makeup can substantially alter the facial features of a person, including making them appear older/younger by adding/hiding wrinkles, modifying the shape of eyebrows, beard, and moustache, and changing the color of lips and cheeks. In our solution, we design a generative adversarial network for removing the makeup from face images while retaining their essential facial features and then compare the face images before and after removing makeup. We collect a large dataset of various types of makeup, especially malicious makeup that can be used to break into remote unattended security systems. This dataset is quite different from existing makeup datasets that mostly focus on cosmetic aspects. We conduct an extensive experimental study to evaluate our method and compare it against the state-of-the art using standard objective metrics commonly used in biometric systems as well as subjective metrics collected through a user study. Our results show that the proposed solution produces high accuracy and substantially outperforms the closest works in the literature.
+{|
+| Face-based authentication systems are among the most used biometric systems, because of the ease of capturing face images in a non-intrusive way. These systems are, however, susceptible to various attacks, including printed faces, artificial masks, and makeup attacks. Makeup attacks are the hardest to detect in biometric systems because makeup can substantially alter the facial features of a person, including making them appear older/younger, modifying the shape of their eyebrows/beard/mustache, and changing the color of their lips and cheeks (Fig. 1). Makeup can even trick human agents trying to identify a person standing before them.
+We proposed a novel solution to detect makeup attacks. The idea of our approach is to design a generative adversarial network (Figure 2) for removing makeup from face images while retaining their essential facial features and then comparing the face images before and after removing makeup. Our approach allows biometric systems to efficiently detect various combinations of makeup, which can be numerous and changing with time. This is in contrast to prior approaches, which require substantial amounts of “labeled data” to train the biometric system for all possible makeup combinations. This is difficult to achieve in practice and thus makes current biometric systems vulnerable to sophisticated makeup attacks.
+||
+[[File:TrueIdentity.png|thumb|right]]
+|}
+We collected a unique dataset of what we call malicious makeup, which is makeup purposely applied to deceive security systems, especially unattended ones where there are no humans to question the potentially weird looks of the makeup. Using extensive objective and subjective studies, we showed that our solution produces high detection accuracy and substantially outperforms the state-of-the-art.
+This work was supported by '''IARPA''' through a sub-contract from the University of Southern California (USC), and it was done in collaboration with Dr. Wael Abd-Almageed and Dr. Mohamed Hussein of ISI at USC.
 == Code and Datasets ==