In this paper, we design and evaluate a convolutional autoencoder that perturbs an input face image to impart privacy to a subject. Specifically, the proposed autoencoder transforms an input face image such that the transformed image can be successfully used for face recognition but not for gender classification. In order to train this autoencoder, we propose a novel training scheme, referred to as semi-adversarial training in this work. The training is facilitated by attaching a semi-adversarial module consisting of a pseudo gender classifier and a pseudo face matcher to the autoencoder. The objective function utilized for training this network has three terms: one to ensure that the perturbed image is a realistic face image; another to ensure that the gender attributes of the face are confounded; and a third to ensure that biometric recognition performance due to the perturbed image is not impacted. Extensive experiments confirm the efficacy of the proposed architecture in extending gender privacy to face images.
A number of aspects of privacy protection has been studied in the biometric literature @cite_20 @cite_8 @cite_12 @cite_14 . On one hand, there are face de-identification techniques @cite_28 @cite_13 @cite_27 where a face image is modified in order to confound a face matcher. On the other hand, as inspired by the work of Othman and Ross @cite_22 and later promoted by Sim and Zhang @cite_3 , the goal is to selectively confound or preserve a set of attributes that can be deduced from face images. Specifically, a few methods for suppressing the gender attribute have been presented @cite_0 @cite_17 @cite_22 . Recently, a new method for protecting privacy with practical applications for biometric databases was proposed in @cite_14 , where input face images were modified with respect to a specific gender classifier. In this case, perturbations were derived based on a specific gender classifier, the perturbations did not significantly impact the match scores of a face matcher.
@cite_14 @cite_22 @cite_8 @cite_28 @cite_3 @cite_0 @cite_27 @cite_20 @cite_13 @cite_12 @cite_17
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While the primary purpose for collecting biometric data (such as face images, iris, fingerprints, etc.) is for person recognition, yet recent advances in machine learning has shown the possibility of extracting auxiliary information from biometric data such as age, gender, health attributes, etc. These auxiliary attributes are sometimes referred to as soft biometrics. This automatic extraction of soft biometric attributes can happen without the user's agreement, thereby raising several privacy concerns. In this work, we design a technique that modifies a face image such that its gender as assessed by a gender classifier is perturbed, while its biometric utility as assessed by a face matcher is retained. Given an arbitrary biometric matcher and an attribute classifier, the proposed method systematically perturbs the input image such that the output of the attribute classifier is confounded, while the output of the biometric matcher is not significantly impacted. Experimental analysis convey the efficacy of the scheme in imparting gender privacy to face images. We consider the problem of perturbing a face image in such a way that it cannot be used to ascertain soft biometric attributes such as age, gender and race, but can be used for automatic face recognition. Such an exercise is useful for extending different levels of privacy to a face image in a central database. In this work, we focus on masking the gender information in a face image with respect to an automated gender estimation scheme, while retaining its ability to be used by a face matcher. To facilitate this privacy-enhancing technique, the input face image is combined with another face image via a morphing scheme resulting in a mixed image. The mixing process can be used to progressively modify the input image such that its gender information is progressively suppressed; however, the modified images can still be used for recognition purposes if necessary. Preliminary experiments on the MUCT database suggest the potential of the scheme in imparting “differential privacy” to face images. A citing sentence is one that appears in a scientific article and cites previous work. Citing sentences have been studied and used in many applications. For example, they have been used in scientific paper summarization, automatic survey generation, paraphrase identification, and citation function classification. Citing sentences that cite multiple papers are common in scientific writing. This observation should be taken into consideration when using citing sentences in applications. For instance, when a citing sentence is used in a summary of a scientific paper, only the fragments of the sentence that are relevant to the summarized paper should be included in the summary. In this paper, we present and compare three different approaches for identifying the fragments of a citing sentence that are related to a given target reference. Our methods are: word classification, sequence labeling, and segment classification. Our experiments show that segment classification achieves the best results. Abstract Documents in computer-readable form can be used to provide information about other documents, i.e. those they cite. To do this efficiently requires procedures for computer recognition of citing statements. This is not easy, especially for multi-sentence citing statements. Computer recognition procedures have been developed which are accurate to the following extent: 73 of the words in statements selected by computer procedures as being citing statements are words which are correctly attributable to the corresponding documents. The retrieval effectiveness of computer-recognized citing statements was tested in the following way. First, for eight retrieval requests in inorganiic chemistry, average recall by search of Chemical Abstracts Service indexing and Chemical Abstracts abstract text words was found to be 50 . Words from citing statements referring to the papers to be retrieved were then added to the index terms and abstract words as additional access points, and searching was repeated. Average recall increased to 70 . Only words from citing statements published within a year of the cited papers were used. The retrieval effect of citing statement words alone (published within a year) without index or abstract terms was the following: average recall was 40 . When just the words of the titles of the cited papers were added to those citing statement words, average recall increased to 50 .