Online documents greatly improve the efficiency of information interaction but also cause potential security hazards, such as the ability to copy and reuse text content without authorization readily. To address copyright concerns, recent works have proposed converting reproducible text content into non-reproducible formats, making digital text content observable but not duplicable. However, as the Optical Character Recognition (OCR) technology develops, adversaries can still take screenshots of the target text region and use OCR to extract the text content. None of the existing methods can be well adapted to this kind of OCR extraction attack. In this paper, we propose "ProTegO'', a novel text content protection method against the OCR extraction attack, which generates adversarial underpaintings that do not affect human reading but can interfere with OCR after taking screenshots. Specifically, we design a text-style universal adversarial underpaintings generation framework, which can mislead both text recognition models and commercial OCR services. For invisibility, we take full advantage of the fusion property of human eyes and create complementary underpaintings to display alternatively on the screen. Experimental results demonstrate that ProTegO is a one-size-fits-all method that can ensure good visual quality while simultaneously achieving a high protection success rate on text recognition models with different architectures, outperforming the state-of-the-art methods. Furthermore, we validate the feasibility of ProTegO on a wide range of popular commercial OCR services, including Microsoft, Tencent, Alibaba, Huawei, Baidu, Apple, and Xiaomi. Codes will be available at https://github.com/Ruby-He/ProTegO.

Deep Neural Networks are vulnerable to adversarial samples, which can fool classifiers by adding small perturbations onto the original image. Since the pioneering optimization-based adversarial attack method, many following methods have been proposed in the past several years. However most of these methods add perturbations in a "pixel-wise" and "global" way. Firstly, because of the contradiction between the local smoothness of natural images and the noisy property of these adversarial perturbations, this "pixel-wise" way makes these methods not robust to image processing based defense methods and steganalysis based detection methods. Secondly, we find adding perturbations to the background is less useful than to the salient object, thus the "global" way is also not optimal. Based on these two considerations, we propose the first robust superpixel-guided attentional adversarial attack method. Specifically, the adversarial perturbations are only added to the salient regions and guaranteed to be same within each superpixel. Through extensive experiments, we demonstrate our method can preserve the attack ability even in this highly constrained modification space. More importantly, compared to existing methods, it is significantly more robust to image processing based defense and steganalysis based detection.

Screen-camera communication techniques achieve one-way communication with widely-used screen and camera devices. Popular barcode methods use visible spatial patterns to represent data, which has been considered obtrusive to human observers. Recent works borrow ideas from visual light communication (VLC), and use inter-frame pixel change to modulate data. To recover pixel change, the receiver has to record and process video frames. Such video-based extraction has high hardware requirements and lacks reliability. Inspired by VLC-based methods, we propose UnseenCode, an invisible on-screen barcode scheme. It leverages inter-frame embedment from VLC-based methods to embed invisible barcodes into arbitrary on-screen contents. Unlike VLC-based methods, UnseenCode does not require video-based extraction. We propose an image-based extraction method based on cross-component correlation of color images. Any off-the-shelf smartphones with camera capability can be used to read UnseenCode by capturing on-screen contents. We propose the full implementation of UnseenCode for evaluation. Experimental results show that UnseenCode decoding algorithm is reliable and applicable under various screen and camera settings. UnseenCode provides up to 2.5 kbit capacity with less than 5% error rate.

Convolutional neural network (CNN) is inspired by the biological structure of human visual system (HVS). And there are still mechanisms in HVS that are worthy learn from. We were inspired by the function of feature selection in HVS which is named as visual saliency and proposed Sal-Mask connection and Ada-Sal Network to implement similar function in neural networks. In this paper, we did further research and tried three different improvement schemes on Ada-Sal Network. By visualizing features we illustrated the feature selection ability of Sal-Mask connection helps the neural network to extract features from more subtle details of input images. The experiment results proved that neural network works better with this saliency based feature selection function. We also found that Sal-Mask connection works best on features from the first convolutional connection.