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Register a new userA Study of Face Obfuscation in ImageNet
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Added by
Kaiyu Yang
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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Face obfuscation (blurring, mosaicing, etc.) has been shown to be effective for privacy protection; nevertheless, object recognition research typically assumes access to complete, unobfuscated images. In this paper, we explore the effects of face obfuscation on the popular ImageNet challenge visual recognition benchmark. Most categories in the ImageNet challenge are not people categories; however, many incidental people appear in the images, and their privacy is a concern. We first annotate faces in the dataset. Then we demonstrate that face blurring -- a typical obfuscation technique -- has minimal impact on the accuracy of recognition models. Concretely, we benchmark multiple deep neural networks on face-blurred images and observe that the overall recognition accuracy drops only slightly (no more than 0.68%). Further, we experiment with transfer learning to 4 downstream tasks (object recognition, scene recognition, face attribute classification, and object detection) and show that features learned on face-blurred images are equally transferable. Our work demonstrates the feasibility of privacy-aware visual recognition, improves the highly-used ImageNet challenge benchmark, and suggests an important path for future visual datasets. Data and code are available at https://github.com/princetonvisualai/imagenet-face-obfuscation.
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The proliferation of automated facial recognition in various commercial and government sectors has caused significant privacy concerns for individuals. A recent and popular approach to address these privacy concerns is to employ evasion attacks against the metric embedding networks powering facial recognition systems. Face obfuscation systems generate imperceptible perturbations, when added to an image, cause the facial recognition system to misidentify the user. The key to these approaches is the generation of perturbations using a pre-trained metric embedding network followed by their application to an online system, whose model might be proprietary. This dependence of face obfuscation on metric embedding networks, which are known to be unfair in the context of facial recognition, surfaces the question of demographic fairness -- textit{are there demographic disparities in the performance of face obfuscation systems?} To address this question, we perform an analytical and empirical exploration of the performance of recent face obfuscation systems that rely on deep embedding networks. We find that metric embedding networks are demographically aware; they cluster faces in the embedding space based on their demographic attributes. We observe that this effect carries through to the face obfuscation systems: faces belonging to minority groups incur reduced utility compared to those from majority groups. For example, the disparity in average obfuscation success rate on the online Face++ API can reach up to 20 percentage points. Further, for some demographic groups, the average perturbation size increases by up to 17% when choosing a target identity belonging to a different demographic group versus the same demographic group. Finally, we present a simple analytical model to provide insights into these phenomena.
Automatic analysis of scanned historical documents comprises a wide range of image analysis tasks, which are often challenging for machine learning due to a lack of human-annotated learning samples. With the advent of deep neural networks, a promising way to cope with the lack of training data is to pre-train models on images from a different domain and then fine-tune them on historical documents. In the current research, a typical example of such cross-domain transfer learning is the use of neural networks that have been pre-trained on the ImageNet database for object recognition. It remains a mostly open question whether or not this pre-training helps to analyse historical documents, which have fundamentally different image properties when compared with ImageNet. In this paper, we present a comprehensive empirical survey on the effect of ImageNet pre-training for diverse historical document analysis tasks, including character recognition, style classification, manuscript dating, semantic segmentation, and content-based retrieval. While we obtain mixed results for semantic segmentation at pixel-level, we observe a clear trend across different network architectures that ImageNet pre-training has a positive effect on classification as well as content-based retrieval.
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Facial image retrieval is a challenging task since faces have many similar features (areas), which makes it difficult for the retrieval systems to distinguish faces of different people. With the advent of deep learning, deep networks are often applied to extract powerful features that are used in many areas of computer vision. This paper investigates the application of different deep learning models for face image retrieval, namely, Alexlayer6, Alexlayer7, VGG16layer6, VGG16layer7, VGG19layer6, and VGG19layer7, with two types of dictionary learning techniques, namely $K$-means and $K$-SVD. We also investigate some coefficient learning techniques such as the Homotopy, Lasso, Elastic Net and SSF and their effect on the face retrieval system. The comparative results of the experiments conducted on three standard face image datasets show that the best performers for face image retrieval are Alexlayer7 with $K$-means and SSF, Alexlayer6 with $K$-SVD and SSF, and Alexlayer6 with $K$-means and SSF. The APR and ARR of these methods were further compared to some of the state of the art methods based on local descriptors. The experimental results show that deep learning outperforms most of those methods and therefore can be recommended for use in practice of face image retrieval
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