Privacy-CNH: A Framework to Detect Photo Privacy with Convolutional Neural Network using Hierarchical Features

Tran, L.T.; Kong, Deguang; Jin, Hongxia; Ji, Liu

doi:10.1609/aaai.v30i1.10169

Cited by 39 publications

(22 citation statements)

References 24 publications

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“…Templeman et al (2014) designed a method to automatically detect locations where the cameras should be turned off. (Tran et al 2016) was similar. Dai et al (2015) studied human activity recognition from extreme low resolution videos, different from conventional activity recognition literature that were focusing mostly on methods for images and videos with sufficient resolutions.…”

Section: Related Workmentioning

confidence: 62%

Privacy-Preserving Human Activity Recognition from Extreme Low Resolution

Ryoo

Rothrock

Fleming

et al. 2017

AAAI

100

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Privacy protection from surreptitious video recordings is an important societal challenge. We desire a computer vision system (e.g., a robot) that can recognize human activities and assist our daily life, yet ensure that it is not recording video that may invade our privacy. This paper presents a fundamental approach to address such contradicting objectives: human activity recognition while only using extreme low-resolution (e.g., 16x12) anonymized videos. We introduce the paradigm of inverse super resolution (ISR), the concept of learning the optimal set of image transformations to generate multiple low-resolution (LR) training videos from a single video. Our ISR learns different types of sub-pixel transformations optimized for the activity classification, allowing the classifier to best take advantage of existing high-resolution videos (e.g., YouTube videos) by creating multiple LR training videos tailored for the problem. We experimentally confirm that the paradigm of inverse super resolution is able to benefit activity recognition from extreme low-resolution videos.

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Section: Related Workmentioning

confidence: 62%

Privacy-Preserving Human Activity Recognition from Extreme Low Resolution

Ryoo

Rothrock

Fleming

et al. 2017

AAAI

100

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show abstract

“…Image Privacy The PicAlert is somewhat biased as most of the private images in PicAlert is person-containing. To diversify private images, Yang et al (2020) extended PicAlert to include more types of images reported in the previous study (Tran et al 2016), such as driver licenses, ID cards, and legal documents. The Image Privacy dataset contains 13,910 private images and 24,615 public images.…”

Section: Datasetsmentioning

confidence: 99%

“…1. Following Tran et al (2016) and Yang et al (2020), we formulate privacy-leaking image detection as a binary classification task (i.e., predict whether…”

Section: Introductionmentioning

confidence: 99%

DRAG: Dynamic Region-Aware GCN for Privacy-Leaking Image Detection

Yang

Cao

Shen

et al. 2022

AAAI

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The daily practice of sharing images on social media raises a severe issue about privacy leakage. To address the issue, privacy-leaking image detection is studied recently, with the goal to automatically identify images that may leak privacy. Recent advance on this task benefits from focusing on crucial objects via pretrained object detectors and modeling their correlation. However, these methods have two limitations: 1) they neglect other important elements like scenes, textures, and objects beyond the capacity of pretrained object detectors. 2) the correlation among objects is fixed, but a fixed correlation is not appropriate for all the images. To overcome the limitations, we propose the Dynamic Region-Aware Graph Convolutional Network (DRAG) that dynamically finds out crucial regions including objects and other important elements, and model their correlation adaptively for each input image. To find out crucial regions, we cluster spatially-correlated feature channels into several region-aware feature maps. Furthermore, we dynamically model the correlation with the self-attention mechanism and explore the interaction among the regions with a graph convolutional network. The DRAG achieved an accuracy of 87% on the largest dataset for privacy-leaking image detection, which is 10 percentage points higher than the state of the art. The further case study demonstrates that it found out crucial regions containing not only objects but other important elements like textures. The code and more details are in https://github.com/guang-yanng/DRAG.

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“…The proposed algorithm can be applied for personalized health-treatment in health-care data analytics, to shorten magnetic resonance imaging scanning sessions on conventional hardware, and to identify the privacy risks of images (Tran et al 2016) (He et al 2015) from object detection, etc. The thorough discussions and comparisons are left for future work.…”

Section: Applications In Business Unit (Bu)mentioning

confidence: 99%

Uncorrelated Group LASSO

Kong

Liu

et al. 2016

AAAI

Self Cite

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l2,1-norm is an effective regularization to enforce a simple group sparsity for feature learning. To capture some subtle structures among feature groups, we propose a new regularization called exclusive group l2,1-norm. It enforces the sparsity at the intra-group level by using l2,1-norm, while encourages the selected features to distribute in different groups by using l2 norm at the inter-group level. The proposed exclusivegroup l2,1-norm is capable of eliminating the feature correlationsin the context of feature selection, if highly correlated features are collected in the same groups. To solve the generic exclusive group l2,1-norm regularized problems, we propose an efficient iterative re-weighting algorithm and provide a rigorous convergence analysis. Experiment results on real world datasets demonstrate the effectiveness of the proposed new regularization and algorithm.

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Privacy-CNH: A Framework to Detect Photo Privacy with Convolutional Neural Network using Hierarchical Features

Cited by 39 publications

References 24 publications

Privacy-Preserving Human Activity Recognition from Extreme Low Resolution

Privacy-Preserving Human Activity Recognition from Extreme Low Resolution

DRAG: Dynamic Region-Aware GCN for Privacy-Leaking Image Detection

Uncorrelated Group LASSO

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