UFace: Your universal password that no one can see

Lin, Dan; Hilbert, Nicholas; Storer, Christian; Jiang, Wei; Fan, Jianping

doi:10.1016/j.cose.2017.09.016

Cited by 12 publications

(6 citation statements)

References 9 publications

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“…In 2018, Boddeti [9] presented a face verification scheme based on fully homomorphic encryption utilizing batching. In the same year, Lin et al [10] introduced a three-party scheme using the Paillier cryptosystem and garbled circuits, with the drawback that the server can obtain verification results.…”

Section: Face Verification Schemementioning

confidence: 99%

A Secure Face Verification Scheme Based on Fully Homomorphic Encryption with Anonymity

Wang,

2024

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With the widespread adoption of cloud computing, the face verification process often requires the client to upload the face to an untrusted cloud server to obtain the verification results. Privacy leakage issues may arise if the client’s private information is not protected. This paper proposes a secure and anonymous face verification scheme using fully homomorphic encryption technology and SealPIR. Our scheme is a three-party solution that requires a third-party server trusted by the client. This scheme not only prevents the client’s facial data from being obtained by untrusted data servers but also prevents the data server from learning the index corresponding to the face that the client wants to verify. In a single-face verification process, the client only needs to perform one upload operation and one download operation, with a communication volume of 264 KB. We can complete a privacy-protected anonymous face verification process in 84.91 ms.

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Section: Face Verification Schemementioning

confidence: 99%

A Secure Face Verification Scheme Based on Fully Homomorphic Encryption with Anonymity

Wang,

2024

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“…There have been various research results in the literature for secure remote biometric verification [ 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 ]. In 2016, Cheon et al proposed a homomorphic encryption (HE)-based protocol to support encrypted Hamming distance computation required for iris recognition [ 40 ].…”

Section: Introductionmentioning

confidence: 99%

Acceleration of Inner-Pairing Product Operation for Secure Biometric Verification

Jeon

Lee

2021

Sensors

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With the recent advances in mobile technologies, biometric verification is being adopted in many smart devices as a means for authenticating their owners. As biometric data leakage may cause stringent privacy issues, many proposals have been offered to guarantee the security of stored biometric data, i.e., biometric template. One of the most promising solutions is the use of a remote server that stores the template in an encrypted form and performs a biometric comparison on the ciphertext domain, using recently proposed functional encryption (FE) techniques. However, the drawback of this approach is that considerable computation is required for the inner-pairing product operation used for the decryption procedure of the underlying FE, which is performed in the authentication phase. In this paper, we propose an enhanced method to accelerate the inner-pairing product computation and apply it to expedite the decryption operation of FE and for faster remote biometric verification. The following two important observations are the basis for our improvement—one of the two arguments for the decryption operation does not frequently change over authentication sessions, and we only need to evaluate the product of multiple pairings, rather than individual pairings. From the results of our experiments, the proposed method reduces the time required to compute an inner-pairing product by 30.7%, compared to the previous best method. With this improvement, the time required for biometric verification is expected to decrease by up to 10.0%, compared to a naive method.

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“…Facial authentication has been increasingly commonly used to unlock personal devices such as smartphones and laptops. Due to its ease of use, the next major horizon for facial authentication applications may be web services [1]. According to statistics [2], an Internet user has an average of 26 different online accounts but only 5 unique passwords for these accounts.…”

Section: Dnn-based Facial Authentication Modelmentioning

confidence: 99%

“…The other kind of data set is the data set that contains the type of fake images which have not been seen by the protector model. The percentage of real, fake and poisoned images in the test data set is also balanced, i.e., 1 3 of each type.…”

Section: Data Set Preparationmentioning

confidence: 99%

Detecting targeted data poisoning attacks on deep neural networks

Newman¹

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Deep neural networks (DNNs) are widely used for various facial image-recognition purposes, including facial recognition and subsequent authentication, and the detection of altered facial images. Unfortunately, due to their widespread use, there have been many works that focus on attacking such DNN-based systems for nefarious purposes. One type of attack on DNNs is called a "targeted data poisoning" attack, which has the goal of injecting photos into the DNNs training set in such a way as to cause the DNN to learn malicious behavior. In the context of facial authentication, this could correspond to unauthorized users gaining access to a target's account, whereas, in deepfake detection, this could translate to causing the DNN to fail to identify when a target's face is the subject of a deepfake image. This report describes targeted data poisoning attacks and proposed defenses on DNN-based systems for facial authentication and deepfake detection, each achieving high accuracy ([greater than] 95 percent) in most cases.

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UFace: Your universal password that no one can see

Cited by 12 publications

References 9 publications

A Secure Face Verification Scheme Based on Fully Homomorphic Encryption with Anonymity

A Secure Face Verification Scheme Based on Fully Homomorphic Encryption with Anonymity

Acceleration of Inner-Pairing Product Operation for Secure Biometric Verification

Detecting targeted data poisoning attacks on deep neural networks

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