text: 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.
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  @cite_30: Biometric recognition refers to the automated recognition of individuals based on their biological and behavioral characteristics such as fingerprint, face, iris, and voice. The first scientific paper on automated fingerprint matching was published by Mitchell Trauring in the journal Nature in 1963. The first objective of this paper is to document the significant progress that has been achieved in the field of biometric recognition in the past 50 years since Trauring's landmark paper. This progress has enabled current state-of-the-art biometric systems to accurately recognize individuals based on biometric trait(s) acquired under controlled environmental conditions from cooperative users. Despite this progress, a number of challenging issues continue to inhibit the full potential of biometrics to automatically recognize humans. The second objective of this paper is to enlist such challenges, analyze the solutions proposed to overcome them, and highlight the research opportunities in this field. One of the foremost challenges is the design of robust algorithms for representing and matching biometric samples obtained from uncooperative subjects under unconstrained environmental conditions (e.g., recognizing faces in a crowd). In addition, fundamental questions such as the distinctiveness and persistence of biometric traits need greater attention. Problems related to the security of biometric data and robustness of the biometric system against spoofing and obfuscation attacks, also remain unsolved. Finally, larger system-level issues like usability, user privacy concerns, integration with the end application, and return on investment have not been adequately addressed. Unlocking the full potential of biometrics through inter-disciplinary research in the above areas will not only lead to widespread adoption of this promising technology, but will also result in wider user acceptance and societ al impact.
  @cite_2: William Stallings' Cryptography and Network Security: Principles and Practice, 5e is a practical survey of cryptography and network security with unmatched support for instructors and students. In this age of universal electronic connectivity, viruses and hackers, electronic eavesdropping, and electronic fraud, security is paramount. This text provides a practical survey of both the principles and practice of cryptography and network security. First, the basic issues to be addressed by a network security capability are explored through a tutorial and survey of cryptography and network security technology. Then, the practice of network security is explored via practical applications that have been implemented and are in use today. An unparalleled support package for instructors and students ensures a successful teaching and learning experience. The new edition has been updated to include coverage of the latest topics including expanded coverage of block cipher modes of operation, including authenticated encryption; revised and expanded coverage of AES; expanded coverage of pseudorandom number generation; new coverage of federated identity, HTTPS, Secure Shell (SSH) and wireless network security; completely rewritten and updated coverage of IPsec; and a new chapter on legal and ethical issues.
summary: 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.