Privacy preservation for image data: A GAN‐based method

Chen, Zhenfei; Zhu, Tianqing; Xiong, Ping; Wang, Chenguang; Ren, Wei

doi:10.1002/int.22356

Cited by 17 publications

(5 citation statements)

References 30 publications

(24 reference statements)

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“…Glance and Focus Matting (GFM) 11 proposes a parallel framework inspired by the comprehensive empirical analysis of the component pipelines in image matting, and it designs a composition route including resolution discrepancy, semantic ambiguity, sharpness discrepancy, and noise discrepancy to reduce the domain gap due to the differences in resolution, sharpness, noise, and so forth. More and more works [38][39][40][41][42] focus on privacy-preserving, privacy-preserving portrait matting (P3M) 14 further expands the GFM to the privacy benchmark by adding the information exchange between parallel branches. However, those methods model trimap-free matting as global segmentation and detail matting as shown in Figure 1.…”

Section: Trimap-free Mattingmentioning

confidence: 99%

Foreground–background decoupling matting

Zheng

Zeng

et al. 2022

Int J of Intelligent Sys

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Image matting aims to extract specific objects, deployed in many applications. Generally, the automatic matting methods need an extra before overcome the intricate details and the diverse appearances. Recently, the matting community has paid more attentions to the investigation of trimapfree matting direction to address the dependency of priors. Most trimap-free approaches divide the matting task into global segmentation and detail matting subtasks. Unfortunately, these methods suffer from stagewise modeling, uncorrectable errors, or subtasks bottleneck problems. To address these issues, we propose a new set of matting subtasks, including foreground segmentation, background segmentation, and disambiguation. And we present a novel Foreground-Background Decoupling Matting (FBDM) network motivated by the new subtasks. Specifically, we first design a nested attention mechanism to decouple the backbone features. Then, we utilize two independent progressive semantic decoders by the decoupling features to complete the foreground and background segmentation subtasks. Finally, we utilize multiple of the proposed frequency division local disambiguation modules to achieve the disambiguation subtask. Besides, we establish a challenging potted plant (PPT) benchmark which contains 100 potted plants images in the real world for the matting

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Section: Trimap-free Mattingmentioning

confidence: 99%

Foreground–background decoupling matting

Zheng

Zeng

et al. 2022

Int J of Intelligent Sys

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“…In recent years, studies on neural style transfer [1][2][3] have fourished. Researchers are no longer satisfed with a single image expression form and have begun to consider more complex and diverse image translation relations [4][5][6][7][8][9][10][11][12][13][14][15][16]. For example, CycleGAN [17] transfers a color photograph to a Monet painting.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Portrait Drawing Generation for Free Styles

Liu,

Liao,

Wang

et al. 2023

International Journal of Intelligent Systems

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Artistic portrait drawing (APDrawing) generation has seen progress in recent years. However, due to the naturally high scarcity and artistry, it is difficult to collect large-scale labeled and paired data and generally divide drawing styles into several specific recognized categories. Existing works suffer from the limited labeled data and naive manual division of drawing styles according to the corresponding artists. They cannot adapt to the actual situations, for example, a single artist might have multiple drawing styles and APDrawings from different artists might share similar styles. In this paper, we propose to use unlabeled and unpaired data and perform the task in an unsupervised manner. Without manual division of drawing styles, we take each portrait drawing as a unique style and introduce self-supervised feature learning to learn free styles for unlabeled portrait drawings. Besides, we devise a style bank and a decoupled cycle structure to take over two main considerations in the task: generation quality and style control. Extensive experiments show that our model is more adaptable to different style inputs than state-of-the-art methods.

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“…With the development of video generation technique, 1,2 it is more convenient for the public to create realistic synthesized videos or modify content of videos nowadays, especially with the help of Generative Adversarial Networks (GAN). 3,4 Such video generation technique has been used for positive purposes like privacy preservation, [5][6][7][8][9][10] but could also cause social problems. In 2017, a Reddit user named "DeepFakes" used deep-learning-based DeepFake methods to create some pornographics videos with swapped faces of celebrities and published them online.…”

Section: Introductionmentioning

confidence: 99%

A lightweight 3D convolutional neural network for deepfake detection

Liu

Zhu

et al. 2021

Int J of Intelligent Sys

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The rapid development of DeepFake technologies has brought great challenges to the authenticity of video contents. It is of vital importance to develop DeepFake detection methods, among which three‐dimensional (3D) convolution neural networks (CNN) have attracted wide interest and achieved satisfying performances. However, there are few 3D CNNs designed for DeepFake detection and the parameters of them are large, which cause heavy memory and storage consumption. In this paper, a lightweight 3D CNN is proposed for DeepFake detection. Channel transformation module is designed to extract features with much fewer parameters in higher level. Serving as spatial‐temporal module, 3D CNNs are adopted to fuse the spatial features in time dimension. To suppress frame content and highlight frame texture, spatial rich model features are extracted from the input frames, which helps the spatial‐temporal module achieve better performance. Experimental results show that the number of parameters of the proposed network is much less than those of other networks and the proposed network outperforms other state‐of‐the‐art DeepFake detection methods on mainstream DeepFake data sets.

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Privacy preservation for image data: A GAN‐based method

Cited by 17 publications

References 30 publications

Foreground–background decoupling matting

Foreground–background decoupling matting

Unsupervised Portrait Drawing Generation for Free Styles

A lightweight 3D convolutional neural network for deepfake detection

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