Deep face recognition: A survey

Deep neural network (DNN) architecture based models have high expressive power and learning capacity. However, they are essentially a black box method since it is not easy to mathematically formulate the functions that are learned within its many layers of representation. Realizing this, many researchers have started to design methods to exploit the drawbacks of deep learning based algorithms questioning their robustness and exposing their singularities. In this paper, we attempt to unravel three aspects related to the robustness of DNNs for face recognition: (i) assessing the impact of deep architectures for face recognition in terms of vulnerabilities to attacks, (ii) detecting the singularities by characterizing abnormal filter response behavior in the hidden layers of deep networks; and (iii) making corrections to the processing pipeline to alleviate the problem. Our experimental evaluation using multiple open-source DNN-based face recognition networks, and three publicly available face databases demonstrates that the performance of deep learning based face recognition algorithms can suffer greatly in the presence of such distortions. We also evaluate the proposed approaches on four existing quasi-imperceptible distortions: DeepFool, Universal adversarial perturbations, $$l_2$$
 , and Elastic-Net (EAD). The proposed method is able to detect both types of attacks with very high accuracy by suitably designing a classifier using the response of the hidden layers in the network. Finally, we present effective countermeasures to mitigate the impact of adversarial attacks and improve the overall robustness of DNN-based face recognition.

Detecting and Mitigating Adversarial Perturbations for Robust Face Recognition

The state-of-the-art performance of deep learning algorithms has led to a considerable increase in the utilization of machine learning in security-sensitive and critical applications. However, it has recently been shown that a small and carefully crafted perturbation in the input space can completely fool a deep model. In this study, we explore the extent to which face recognition systems are vulnerable to geometrically-perturbed adversarial faces. We propose a fast landmark manipulation method for generating adversarial faces, which is approximately 200 times faster than the previous geometric attacks and obtains 99.86% success rate on the state-of-the-art face recognition models. To further force the generated samples to be natural, we introduce a second attack constrained on the semantic structure of the face which has the half speed of the first attack with the success rate of 99.96%. Both attacks are extremely robust against the state-of-the-art defense methods with the success rate of equal or greater than 53.59%. Code is available at https://github.com/alldbi/FLM

Fast Geometrically-Perturbed Adversarial Faces

High performance of deep neural network based systems have attracted many applications in object recognition and face recognition. However, researchers have also demonstrated them to be highly sensitive to adversarial perturbation and hence, tend to be unreliable and lack robustness. While most of the research on adversarial perturbation focuses on image specific attacks, recently, image-agnostic Universal perturbations are proposed which learn the adversarial pattern over training distribution and have broader impact on real-world security applications. Such adversarial attacks can have compounding effect on face recognition where these visually imperceptible attacks can cause mismatches. To defend against adversarial attacks, sophisticated detection approaches are prevalent but most of the existing approaches do not focus on image-agnostic attacks. In this paper, we present a simple but efficient approach based on pixel values and Principal Component Analysis as features coupled with a Support Vector Machine as the classifier, to detect image-agnostic universal perturbations. We also present evaluation metrics, namely adversarial perturbation class classification error rate, original class classification error rate, and average classification error rate, to estimate the performance of adversarial perturbation detection algorithms. The experimental results on multiple databases and different DNN architectures show that it is indeed not required to build complex detection algorithms; rather simpler approaches can yield higher detection rates and lower error rates for image agnostic adversarial perturbation.

Are Image-Agnostic Universal Adversarial Perturbations for Face Recognition Difficult to Detect?

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

/pdf/on-the-robustness-of-face-recognition-algorithms-against-4pm6ra3h9o.pdf

On the Robustness of Face Recognition Algorithms Against Attacks and Bias

Deep learning models are widely used for various purposes such as face recognition and speech recognition. However, researchers have shown that these models are vulnerable to adversarial attacks. These attacks compute perturbations to generate images that decrease the performance of deep learning models. In this research, we have developed a toolbox, termed as SmartBox, for benchmarking the performance of adversarial attack detection and mitigation algorithms against face recognition. SmartBox is a python based toolbox which provides an open source implementation of adversarial detection and mitigation algorithms. In this research, Extended Yale Face Database B has been used for generating adversarial examples using various attack algorithms such as DeepFool, Gradient methods, Elastic-Net, and $L_{2}$ attack. SmartBox provides a platform to evaluate newer attacks, detection models, and mitigation approaches on a common face recognition benchmark. To assist the research community, the code of SmartBox is made available11http://iab-rubric.org/resources/SmartBox.html.

SmartBox: Benchmarking Adversarial Detection and Mitigation Algorithms for Face Recognition

Object detection in low-light aerial images is a challenging problem due to considerable variation in brightness and varying contrast. Deep learning-based approaches have recently demonstrated great promise in image enhancement. Many existing neural networks used for image quality enhancement first encode the input into low-resolution representations and then decode these representations back to a higher resolution for the contextual information. However, this method leads to the loss of semantic content. Recent research has demonstrated the advantage of maintaining high-resolution information along with lower resolution representations, which maintains image features throughout the network. In this letter, we propose a novel architecture named RNet for low-light image enhancement of aerial images. The proposed network contains multiresolution branches for better understanding of different levels of local and global context through different streams. The performance of RNet is evaluated on a recent synthetic dataset. We also present a comprehensive evaluation with a representative set of state-of-the-art enhancement techniques and neural net architectures.

Anirudh Singh

Papers

SmartBox: Benchmarking Adversarial Detection and Mitigation Algorithms for Face Recognition

Low-Light Image Enhancement for UAVs With Multi-Feature Fusion Deep Neural Networks