scholarly journals Protecting Privacy in Shared Photos via Adversarial Examples Based Stealth

2017 ◽  
Vol 2017 ◽  
pp. 1-15 ◽  
Author(s):  
Yujia Liu ◽  
Weiming Zhang ◽  
Nenghai Yu
Keyword(s):  
Visual Quality ◽  
Adjustable Parameter ◽  
Image Sharing ◽  
Considerable Success ◽  
Powerful Learning ◽  
Invisible Cloak ◽  
Depth Preference ◽  
Adversarial Examples ◽  
Human Eyes

Online image sharing in social platforms can lead to undesired privacy disclosure. For example, some enterprises may detect these large volumes of uploaded images to do users’ in-depth preference analysis for commercial purposes. And their technology might be today’s most powerful learning model, deep neural network (DNN). To just elude these automatic DNN detectors without affecting visual quality of human eyes, we design and implement a novel Stealth algorithm, which makes the automatic detector blind to the existence of objects in an image, by crafting a kind of adversarial examples. It is just like all objects disappear after wearing an “invisible cloak” from the view of the detector. Then we evaluate the effectiveness of Stealth algorithm through our newly defined measurement, named privacy insurance. The results indicate that our scheme has considerable success rate to guarantee privacy compared with other methods, such as mosaic, blur, and noise. Better still, Stealth algorithm has the smallest impact on image visual quality. Meanwhile, we set a user adjustable parameter called cloak thickness for regulating the perturbation intensity. Furthermore, we find that the processed images have transferability property; that is, the adversarial images generated for one particular DNN will influence the others as well.

K out of N Secret Sharing Scheme for Multiple Color Images with Steganography and Authentication

2016 ◽  
Vol 16 (02) ◽  
pp. 1650010 ◽  
Author(s):  
P. Mohamed Fathimal ◽  
P. Arockia Jansi Rani
Keyword(s):  
Secret Sharing ◽  
Visual Quality ◽  
Color Images ◽  
Pixel Expansion ◽  
Image Sharing ◽  
Digital Ecosystem ◽  
Sharing Scheme ◽  
Text Steganography ◽  
Key Aspects

With our lives trundling toward a fully-digital ecosystem in break-neck speed, today’s encryption and cryptography are facing the challenge of ensuring security and future-readiness of our transactions. When such transactions involve multiple hands, transmission of such data in discrete and recoverable parts (secret shares) guarantees confidentiality. This paper’s objective is to present a foolproof way of multiple secret sharing, eliminating issues such as half-toning and degradation of visual quality of the recovered images. This [Formula: see text] out of [Formula: see text] steganography and authenticated image sharing (SAIS) scheme for multiple color images generates [Formula: see text] relevant shares with the ability to reconstruct the secret images using [Formula: see text] shares and facility to find out any move for appropriation of share cover images. The key aspects of this proposed scheme is to use simple Boolean and arithmetic operations with reduction of computational complexity from [Formula: see text] to [Formula: see text] and to share multiple images without any pixel expansion.

scholarly journals Meaningful Secret Image Sharing Scheme with High Visual Quality Based on Natural Steganography

Mathematics ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1452
Author(s):  
Yuyuan Sun ◽  
Yuliang Lu ◽  
Jinrui Chen ◽  
Weiming Zhang ◽  
Xuehu Yan
Keyword(s):  
Visual Quality ◽  
Secret Image Sharing ◽  
Secret Image ◽  
Image Sharing ◽  
Lossless Recovery ◽  
Sharing Scheme ◽  
Image Protection ◽  
Embedding Rate ◽  
Secret Image Sharing Scheme

The (k,n)-threshold Secret Image Sharing scheme (SISS) is a solution to image protection. However, the shadow images generated by traditional SISS are noise-like, easily arousing deep suspicions, so that it is significant to generate meaningful shadow images. One solution is to embed the shadow images into meaningful natural images and visual quality should be considered first. Limited by embedding rate, the existing schemes have made concessions in size and visual quality of shadow images, and few of them take the ability of anti-steganalysis into consideration. In this paper, a meaningful SISS that is based on Natural Steganography (MSISS-NS) is proposed. The secret image is firstly divided into n small-sized shadow images with Chinese Reminder Theorem, which are then embedded into RAW images to simulate the images with higher ISO parameters with NS. In MSISS-NS, the visual quality of shadow images is improved significantly. Additionally, as the payload of cover images with NS is larger than the size of small-sized shadow images, the scheme performs well not only in visual camouflage, but also in other aspects, like lossless recovery, no pixel expansion, and resisting steganalysis.

scholarly journals The secure steganography for hiding images via GAN

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Zhangjie Fu ◽  
Fan Wang ◽  
Xu Cheng
Keyword(s):  
Deep Learning ◽  
Color Image ◽  
Visual Quality ◽  
Learning Networks ◽  
Secret Data ◽  
Rapid Improvement ◽  
Proposed Model ◽  
Adversarial Training ◽  
Human Eyes

Abstract Steganography is one of the important methods in the field of information hiding, which is the technique of hiding secret data within an ordinary file or message in order to avoid the detection of steganalysis models and human eyes. In recent years, many scholars have applied various deep learning networks to the field of steganalysis to improve the accuracy of detection. The rapid improvement of the accuracy of steganalysis models has caused a huge threat to the security of steganography. In addition, another important factor that limits the security of steganography is capacity. The larger the capacity, the worse and more unnatural the visual quality of carrier images after embedded. Therefore, this paper proposes a steganography model—HIGAN, which constructs the encoding network composed of residual blocks to hide the color secret image into another color image of the same size to output a lower distortion and higher visual quality steganographic image. Moreover, it utilizes the adversarial training between the encoder-decoder network and the steganalysis model to improve the ability to resist the detection of steganalysis models based on deep learning. The experimental results show that our proposed model is achievable and effective. Compared with the previous steganography model for hiding color images based on deep learning, the steganography model in this article could achieve steganographic images with higher visual quality and stronger security.

scholarly journals Image Watermarking in Curvelet Domain Using Edge Surface Blocks

Symmetry ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 822 ◽  
Author(s):  
Mamta Mittal ◽  
Ranjeeta Kaushik ◽  
Amit Verma ◽  
Iqbaldeep Kaur ◽  
Lalit Mohan Goyal ◽  
...  
Keyword(s):  
Image Watermarking ◽  
Curvelet Transform ◽  
Visual Quality ◽  
Strength Parameter ◽  
Partial Differential ◽  
First Order ◽  
Gaussian Filters ◽  
Edge Surface ◽  
Human Eyes

Digital image watermarking aims to protect the information in an image without significantly affecting visual quality. In this paper, a new image watermarking technique has been proposed that uses Gaussian filters and first-order partial differential matrix to extort the edge surface of a host image. This paper influence on the edge surface curvelet coefficients as human eyes are not equally sensitive to a smooth and an edged surface. To preserve the quality of the artwork and to increase the resistance against attacks, the author utilizes the edge surface area of an image, coarse levels of curvelet transform, and strength parameters. The selection of host coefficients are conforming to the human visual system (HVS) is the uniqueness of the research. The exploitation of the Gaussian filters and first-order partial differential coarse curvelet coefficients and the watermark strength parameter offers robustness against image processing attacks. The standard visual quality perception of HVS evaluation metrics are used to measure the superiority of the presented work.

scholarly journals Smooth adversarial examples

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Hanwei Zhang ◽  
Yannis Avrithis ◽  
Teddy Furon ◽  
Laurent Amsaleg
Keyword(s):  
Signal Processing ◽  
High Frequency ◽  
Visual Quality ◽  
Input Image ◽  
Additional Constraint ◽  
Visual Content ◽  
Probability Of Success ◽  
Adversarial Examples ◽  
Laplacian Smoothing

AbstractThis paper investigates the visual quality of the adversarial examples. Recent papers propose to smooth the perturbations to get rid of high frequency artifacts. In this work, smoothing has a different meaning as it perceptually shapes the perturbation according to the visual content of the image to be attacked. The perturbation becomes locally smooth on the flat areas of the input image, but it may be noisy on its textured areas and sharp across its edges.This operation relies on Laplacian smoothing, well-known in graph signal processing, which we integrate in the attack pipeline. We benchmark several attacks with and without smoothing under a white box scenario and evaluate their transferability. Despite the additional constraint of smoothness, our attack has the same probability of success at lower distortion.

Image Complexity Analysis Based on HVS and Its Application in Steganography

2013 ◽  
Vol 333-335 ◽  
pp. 1118-1122
Author(s):  
Jun Li ◽  
Xiao Yuan Yang
Keyword(s):  
Wavelet Transform ◽  
Complexity Analysis ◽  
Visual Quality ◽  
Modulus Function ◽  
Secret Message ◽  
Embedding Capacity ◽  
Stego Image ◽  
Different Types ◽  
Human Eyes

With human eyes has different insensitive to different types of texture, edged and dark area, this paper proposed an image steganography with higher embedding capacity and good stego image quality. In our method, the image blocks was divided into four kinds (texture, edged, dark and smooth), and secret message was embedded with the algorithm of modulus function coincide with wavelet transform. From the experimental results, the complexity notion can distinguish different kinds of blocks precisely, and the steganography method can hide much larger message and maintain a good visual quality of stego image.

Enhancing Visual Quality of Multi-view 360 Video Compression Pipeline

2019 ◽  
pp. 135
Author(s):  
Junyoung Yun ◽  
Hong-Chang Shin ◽  
Gwangsoon Lee ◽  
Jong-Il Park
Keyword(s):  
Video Compression ◽  
Visual Quality

scholarly journals ART-UP: A Novel Method for Generating Scanning-Robust Aesthetic QR Codes

2021 ◽  
Vol 17 (1) ◽  
pp. 1-23
Author(s):  
Mingliang Xu ◽  
Qingfeng Li ◽  
Jianwei Niu ◽  
Hao Su ◽  
Xiting Liu ◽  
...  
Keyword(s):  
State Of The Art ◽  
Visual Quality ◽  
Qr Code ◽  
Quick Response ◽  
Estimation Model ◽  
Qr Codes ◽  
Excellent Performance ◽  
Novel Method ◽  
Coarse To Fine

Quick response (QR) codes are usually scanned in different environments, so they must be robust to variations in illumination, scale, coverage, and camera angles. Aesthetic QR codes improve the visual quality, but subtle changes in their appearance may cause scanning failure. In this article, a new method to generate scanning-robust aesthetic QR codes is proposed, which is based on a module-based scanning probability estimation model that can effectively balance the tradeoff between visual quality and scanning robustness. Our method locally adjusts the luminance of each module by estimating the probability of successful sampling. The approach adopts the hierarchical, coarse-to-fine strategy to enhance the visual quality of aesthetic QR codes, which sequentially generate the following three codes: a binary aesthetic QR code, a grayscale aesthetic QR code, and the final color aesthetic QR code. Our approach also can be used to create QR codes with different visual styles by adjusting some initialization parameters. User surveys and decoding experiments were adopted for evaluating our method compared with state-of-the-art algorithms, which indicates that the proposed approach has excellent performance in terms of both visual quality and scanning robustness.

Effect of defocus combined with rotation on the optical performance of trifocal toric IOLs

2021 ◽  
pp. 112067212110021
Author(s):  
Javier Ruiz-Alcocer ◽  
Irene Martínez-Alberquilla ◽  
Amalia Lorente-Velázquez ◽  
José F Alfonso ◽  
David Madrid-Costa
Keyword(s):  
Visual Quality ◽  
Optical Quality ◽  
Refractive Errors ◽  
Focal Points ◽  
Modulation Transfer ◽  
Optical Analysis ◽  
Toric Intraocular Lens ◽  
Toric Iols ◽  
Reference Situation

Purpose: To objectively analyze the optical quality of the FineVision Toric intraocular lens (IOL) with two cylinder powers when different combinations of rotations and residual refractive errors are induced. Methods: This study assessed the FineVision Toric IOL with two different cylinder powers: 1.5 and 3.0 diopters (D). Three different rotation positions were considered: centered, 5° and 10° rotated. An optical bench (PMTF) was used for optical analysis. The optical quality of the IOLs was calculated by the modulation transfer function (MTF) at five different focal points (0.0, 0.25, 0.50, 0.75, and 1.00 D). Results: The MTF averaged value of the reference situation was 38.58 and 37.74 for 1.5 and 3.0 D of cylinder, respectively. For the 1.5 D cylinder, the combination of 5° of rotation with a defocus of 0.25, 0.50, 0.75, and 1.0 D induced a decrease on the MTF of 12.39, 19.94, 23.43, 24.23 units, respectively. When induced rotation was 10°, the MTF decrease was 17.26, 23.40, 24.33, 24.48 units, respectively. For the 3.0 D cylinder, the combination of 5° with 0.25, 0.50, 0.75, and 1.0 D of defocus, induced a decrease on the MTF of 12.51, 18.97, 22.36, 22.48 units, respectively. When induced rotation was 10°, the MTF decrease was: 18.42, 21.57, 23.08, and 23.61 units, respectively. Conclusion: For both FineVision Toric IOLs there is a certain optical tolerance to rotations up to 5° or residual refractive errors up to 0.25 D. Situations over these limits and their combination would affect the visual quality of patients implanted with these trifocal toric IOLs.

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