State-of-the-art face recognition systems are based on deep (convolutional) neural networks. Therefore, it is imperative to determine to what extent face templates derived from deep networks can be inverted to obtain the original face image. In this paper, we study the vulnerabilities of a state-of-the-art face recognition system based on template reconstruction attack. We propose a neighborly de-convolutional neural network ( NbNet ) to reconstruct face images from their deep templates. In our experiments, we assumed that no knowledge about the target subject and the deep network are available. To train the NbNet reconstruction models, we augmented two benchmark face datasets (VGG-Face and Multi-PIE) with a large collection of images synthesized using a face generator. The proposed reconstruction was evaluated using type-I (comparing the reconstructed images against the original face images used to generate the deep template) and type-II (comparing the reconstructed images against a different face image of the same subject) attacks. Given the images reconstructed from NbNets , we show that for verification, we achieve TAR of 95.20 percent (58.05 percent) on LFW under type-I (type-II) attacks @ FAR of 0.1 percent. Besides, 96.58 percent (92.84 percent) of the images reconstructed from templates of partition fa ( fb ) can be identified from partition fa in color FERET. Our study demonstrates the need to secure deep templates in face recognition systems.
Unlike traditional passwords that can be matched in their encrypted or hash form with standard ciphers (e.g., AES, RSA, and SHA-3), face templates cannot be simply protected by standard ciphers because of the intra-subject variations in face images @cite_57 @cite_30 . Due to the avalanche effect https: en.wikipedia.org wiki Avalanche @cite_2 of standard ciphers, the face templates protected by standard ciphers need to be decrypted before matching. This introduces another challenge, (decryption) key management. In addition, decrypted face templates can also be gleaned by launching an authentication attempt.
@cite_57 @cite_30 @cite_2
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Given the prevalence of social media websites, one challenge facing computer vision researchers is to devise methods to search for persons of interest among the billions of shared photos on these websites. Despite significant progress in face recognition, searching a large collection of unconstrained face images remains a difficult problem. To address this challenge, we propose a face search system which combines a fast search procedure, coupled with a state-of-the-art commercial off the shelf (COTS) matcher, in a cascaded framework. Given a probe face, we first filter the large gallery of photos to find the top- @math most similar faces using features learned by a convolutional neural network. The @math retrieved candidates are re-ranked by combining similarities based on deep features and those output by the COTS matcher. We evaluate the proposed face search system on a gallery containing @math million web-downloaded face images. Experimental results demonstrate that while the deep features perform worse than the COTS matcher on a mugshot dataset (93.7 percent versus 98.6 percent TAR@FAR of 0.01 percent), fusing the deep features with the COTS matcher improves the overall performance ( @math percent TAR@FAR of 0.01 percent). This shows that the learned deep features provide complementary information over representations used in state-of-the-art face matchers. On the unconstrained face image benchmarks, the performance of the learned deep features is competitive with reported accuracies. LFW database: @math percent accuracy under the standard protocol and @math percent TAR@FAR of @math percent under the BLUFR protocol; IJB-A benchmark: @math percent TAR@FAR of @math percent (verification), rank 1 retrieval of @math percent (closed-set search), @math percent FNIR@FAR of @math percent (open-set search). The proposed face search system offers an excellent trade-off between accuracy and scalability on galleries with millions of images. Additionally, in a face search experiment involving photos of the Tsarnaev brothers, convicted of the Boston Marathon bombing, the proposed cascade face search system could find the younger brother's (Dzhokhar Tsarnaev) photo at rank @math in @math second on a @math M gallery and at rank @math in @math seconds on an @math M gallery. Lattice-based signature schemes following the Goldreich–Goldwasser–Halevi (GGH) design have the unusual property that each signature leaks information on the signer’s secret key, but this does not necessarily imply that such schemes are insecure. At Eurocrypt ’03, Szydlo proposed a potential attack by showing that the leakage reduces the key-recovery problem to that of distinguishing integral quadratic forms. He proposed a heuristic method to solve the latter problem, but it was unclear whether his method could attack real-life parameters of GGH and NTRUSign. Here, we propose an alternative method to attack signature schemes a la GGH by studying the following learning problem: given many random points uniformly distributed over an unknown n-dimensional parallelepiped, recover the parallelepiped or an approximation thereof. We transform this problem into a multivariate optimization problem that can provably be solved by a gradient descent. Our approach is very effective in practice: we present the first successful key-recovery experiments on NTRUSign-251 without perturbation, as proposed in half of the parameter choices in NTRU standards under consideration by IEEE P1363.1. Experimentally, 400 signatures are sufficient to recover the NTRUSign-251 secret key, thanks to symmetries in NTRU lattices. We are also able to recover the secret key in the signature analogue of all the GGH encryption challenges. We propose a novel approach to template based face recognition. Our dual goal is to both increase recognition accuracy and reduce the computational and storage costs of template matching. To do this, we leverage on an approach which was proven effective in many other domains, but, to our knowledge, never fully explored for face images: average pooling of face photos. We show how (and why!) the space of a template's images can be partitioned and then pooled based on image quality and head pose and the effect this has on accuracy and template size. We perform extensive tests on the IJB-A and Janus CS2 template based face identification and verification benchmarks. These show that not only does our approach outperform published state of the art despite requiring far fewer cross template comparisons, but also, surprisingly, that image pooling performs on par with deep feature pooling. In many applications, such as E-Passport and driver's license, the enrollment of face templates is done using visible light (VIS) face images. Such images are normally acquired in controlled environment where the lighting is approximately frontal. However, Authentication is done in variable lighting conditions. Matching of faces in VIS images taken in different lighting conditions is still a big challenge. A recent development in near infrared (NIR) image based face recognition [1] has well overcome the difficulty arising from lighting changes. However, it requires that enrollment face images be acquired using NIR as well. In this paper, we present a new problem, that of matching a face in an NIR image against one in a VIS images, and propose a solution to it. The work is aimed to develop a new solution for meeting the accuracy requirement of face-based biometric recognition, by taking advantages of the recent NIR face technology while allowing the use of existing VIS face photos as gallery templates. Face recognition is done by matching an NIR probe face against a VIS gallery face. Based on an analysis of properties of NIR and VIS face images, we propose a learning-based approach for the different modality matching. A mechanism of correlation between NIR and VIS faces is learned from NIR → VIS face pairs, and the learned correlation is used to evaluate similarity between an NIR face and a VIS face. We provide preliminary results of NIR → VIS face matching for recognition under different illumination conditions. The results demonstrate advantages of NIR → VIS matching over VIS → VIS matching.