248 research outputs found
On the Robustness of Face Recognition Algorithms Against Attacks and Bias
Face recognition algorithms have demonstrated very high recognition
performance, suggesting suitability for real world applications. Despite the
enhanced accuracies, robustness of these algorithms against attacks and bias
has been challenged. This paper summarizes different ways in which the
robustness of a face recognition algorithm is challenged, which can severely
affect its intended working. Different types of attacks such as physical
presentation attacks, disguise/makeup, digital adversarial attacks, and
morphing/tampering using GANs have been discussed. We also present a discussion
on the effect of bias on face recognition models and showcase that factors such
as age and gender variations affect the performance of modern algorithms. The
paper also presents the potential reasons for these challenges and some of the
future research directions for increasing the robustness of face recognition
models.Comment: Accepted in Senior Member Track, AAAI202
Simultaneous Adversarial Attacks On Multiple Face Recognition System Components
In this work, we investigate the potential threat of adversarial examples to
the security of face recognition systems. Although previous research has
explored the adversarial risk to individual components of FRSs, our study
presents an initial exploration of an adversary simultaneously fooling multiple
components: the face detector and feature extractor in an FRS pipeline. We
propose three multi-objective attacks on FRSs and demonstrate their
effectiveness through a preliminary experimental analysis on a target system.
Our attacks achieved up to 100% Attack Success Rates against both the face
detector and feature extractor and were able to manipulate the face detection
probability by up to 50% depending on the adversarial objective. This research
identifies and examines novel attack vectors against FRSs and suggests possible
ways to augment the robustness by leveraging the attack vector's knowledge
during training of an FRS's components
Data Fine-tuning
In real-world applications, commercial off-the-shelf systems are utilized for
performing automated facial analysis including face recognition, emotion
recognition, and attribute prediction. However, a majority of these commercial
systems act as black boxes due to the inaccessibility of the model parameters
which makes it challenging to fine-tune the models for specific applications.
Stimulated by the advances in adversarial perturbations, this research proposes
the concept of Data Fine-tuning to improve the classification accuracy of a
given model without changing the parameters of the model. This is accomplished
by modeling it as data (image) perturbation problem. A small amount of "noise"
is added to the input with the objective of minimizing the classification loss
without affecting the (visual) appearance. Experiments performed on three
publicly available datasets LFW, CelebA, and MUCT, demonstrate the
effectiveness of the proposed concept.Comment: Accepted in AAAI 201
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