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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Biometric cryptography is a technique using biometric features to encrypt data, which can improve the security of the encrypted data and overcome the shortcomings of the traditional cryptography. This paper proposes a novel biometric cryptosystem based on the most accurate biometric feature - iris. In encryption phase, a quantified 256-dimension textural feature vector is firstly extracted from the preprocessed iris image using a set of 2-D Gabor filters. At the same time, an error-correct-code (ECC) is generated using Reed-Solomon algorithm. Then the feature vector is translated to a cipher key using Hash function. Some general encryption algorithms use this cipher key to encrypt the secret information. In decryption phase, a feature vector extracted from the input iris is firstly corrected using the ECC. Then it is translated to the cipher key using the same Hash function. Finally, the corresponding general decryption algorithms use the key to decrypt the information. Experimental results demonstrate the feasibility of the proposed system. The importance of cryptography on ensuring security or integrity of the electronic data transaction had become higher during the past few years. Multiple security protocols are currently using various block ciphers. One of the most widely used block ciphers is the Advanced Encryption Standard (AES) which is chosen as a standard for its higher efficiency and stronger security than its competitors. Unfortunately, the encryption and decryption processes of AES takes a considerable amount of time for large data size. The GPU is an attractive platform for accelerating block ciphers and other cryptography algorithms due to its massively parallel processing power. In this work, an implementation of the AES-128 ECB Encryption on three different GPU architectures (Kepler, Maxwell and Pascal) has been presented. The results show that encryption speeds with 207 Gbps on the NVIDIA GTX TITAN X (Maxwell) and 280 Gbps on the NVIDIA GTX 1080 (Pascal) have been achieved by performing new optimization techniques using 32bytes thread granularity. In this paper, the neural network composed of a chaotic neuron layer and a linear neuron layer is used to construct a block cipher that transforms the data from the plaintext form into the unintelligible form under the control of the key. Among them, the chaotic neuron layer realizes data diffusion, the linear neuron layer realizes data confusion, and the two layers are repeated for several times to strengthen the cipher. The decryption process is symmetric to the encryption process. Theoretical analysis and experimental results show that the block cipher has good computing security and is more suitable for image encryption. It is expected to attract more researchers in this field.