3,307 research outputs found
Tag detection for preventing unauthorized face image processing
A new technology is being proposed as a solution to the
problem of unintentional facial detection and recognition in pictures in which the individuals appearing want to express their privacy preferences, through the use of different tags. The existing methods for face
de-identification were mostly ad hoc solutions that only provided an absolute binary solution in a privacy context such as pixelation, or a bar mask. As the number and users of social networks are increasing, our preferences
regarding our privacy may become more complex, leaving these absolute binary solutions as something obsolete. The proposed technology overcomes this problem by embedding information in a tag which will be placed close to the face without being disruptive. Through a decoding
method the tag will provide the preferences that will be applied to the images in further stages
Roadmap on optical security
Postprint (author's final draft
Deep Serial Number: Computational Watermarking for DNN Intellectual Property Protection
In this paper, we introduce DSN (Deep Serial Number), a new watermarking
approach that can prevent the stolen model from being deployed by unauthorized
parties. Recently, watermarking in DNNs has emerged as a new research direction
for owners to claim ownership of DNN models. However, the verification schemes
of existing watermarking approaches are vulnerable to various watermark
attacks. Different from existing work that embeds identification information
into DNNs, we explore a new DNN Intellectual Property Protection mechanism that
can prevent adversaries from deploying the stolen deep neural networks.
Motivated by the success of serial number in protecting conventional software
IP, we introduce the first attempt to embed a serial number into DNNs.
Specifically, the proposed DSN is implemented in the knowledge distillation
framework, where a private teacher DNN is first trained, then its knowledge is
distilled and transferred to a series of customized student DNNs. During the
distillation process, each customer DNN is augmented with a unique serial
number, i.e., an encrypted 0/1 bit trigger pattern. Customer DNN works properly
only when a potential customer enters the valid serial number. The embedded
serial number could be used as a strong watermark for ownership verification.
Experiments on various applications indicate that DSN is effective in terms of
preventing unauthorized application while not sacrificing the original DNN
performance. The experimental analysis further shows that DSN is resistant to
different categories of attacks
SocialGuard: An Adversarial Example Based Privacy-Preserving Technique for Social Images
The popularity of various social platforms has prompted more people to share
their routine photos online. However, undesirable privacy leakages occur due to
such online photo sharing behaviors. Advanced deep neural network (DNN) based
object detectors can easily steal users' personal information exposed in shared
photos. In this paper, we propose a novel adversarial example based
privacy-preserving technique for social images against object detectors based
privacy stealing. Specifically, we develop an Object Disappearance Algorithm to
craft two kinds of adversarial social images. One can hide all objects in the
social images from being detected by an object detector, and the other can make
the customized sensitive objects be incorrectly classified by the object
detector. The Object Disappearance Algorithm constructs perturbation on a clean
social image. After being injected with the perturbation, the social image can
easily fool the object detector, while its visual quality will not be degraded.
We use two metrics, privacy-preserving success rate and privacy leakage rate,
to evaluate the effectiveness of the proposed method. Experimental results show
that, the proposed method can effectively protect the privacy of social images.
The privacy-preserving success rates of the proposed method on MS-COCO and
PASCAL VOC 2007 datasets are high up to 96.1% and 99.3%, respectively, and the
privacy leakage rates on these two datasets are as low as 0.57% and 0.07%,
respectively. In addition, compared with existing image processing methods (low
brightness, noise, blur, mosaic and JPEG compression), the proposed method can
achieve much better performance in privacy protection and image visual quality
maintenance
Hunting CAPTCHA-solving bots
openToday, smart phones have become an integral part of modern human life. By increasing CPU power and energy efficiency of these types of equipment, almost all daily routines and even personal activities of people have become dependent on these devices. By knowing the importance of these equipment in today's human life and crucial role of them to protect personal sensitive information, security and authorized access to these data are indispensable requirement in any new methods in this field of study. Today, CAPTCHAs are used to protect smart phones and computers from robot access, however most of which are broken and hacked by robots and machine learning based method. Therefore, it is necessary to provide more accurate and comprehensive algorithm in order to identify robots and prevent them from entering mobile phones
Privacy Intelligence: A Survey on Image Sharing on Online Social Networks
Image sharing on online social networks (OSNs) has become an indispensable
part of daily social activities, but it has also led to an increased risk of
privacy invasion. The recent image leaks from popular OSN services and the
abuse of personal photos using advanced algorithms (e.g. DeepFake) have
prompted the public to rethink individual privacy needs when sharing images on
OSNs. However, OSN image sharing itself is relatively complicated, and systems
currently in place to manage privacy in practice are labor-intensive yet fail
to provide personalized, accurate and flexible privacy protection. As a result,
an more intelligent environment for privacy-friendly OSN image sharing is in
demand. To fill the gap, we contribute a systematic survey of 'privacy
intelligence' solutions that target modern privacy issues related to OSN image
sharing. Specifically, we present a high-level analysis framework based on the
entire lifecycle of OSN image sharing to address the various privacy issues and
solutions facing this interdisciplinary field. The framework is divided into
three main stages: local management, online management and social experience.
At each stage, we identify typical sharing-related user behaviors, the privacy
issues generated by those behaviors, and review representative intelligent
solutions. The resulting analysis describes an intelligent privacy-enhancing
chain for closed-loop privacy management. We also discuss the challenges and
future directions existing at each stage, as well as in publicly available
datasets.Comment: 32 pages, 9 figures. Under revie
EFFICIENT RUNTIME SECURITY SYSTEM FOR DECENTRALISED DISTRIBUTED SYSTEMS
Distributed systems can be defined as systems that are scattered over geographical distances and provide different activities through communication, processing, data transfer and so on. Thus, increasing the cooperation, efficiency, and reliability to deal with users and data resources jointly. For this reason, distributed systems have been shown to be a promising infrastructure for most applications in the digital world. Despite their advantages, keeping these systems secure, is a complex task because of the unconventional nature of distributed systems which can produce many security problems like phishing, denial of services or eavesdropping. Therefore, adopting security and privacy policies in distributed systems will increase the trustworthiness between the users and these systems. However, adding or updating security is considered one of the most challenging concerns and this relies on various security vulnerabilities which existing in distributed systems. The most significant one is inserting or modifying a new security concern or even removing it according to the security status which may appear at runtime. Moreover, these problems will be exacerbated when the system adopts the multi-hop concept as a way to deal with transmitting and processing information. This can pose many significant security challenges especially if dealing with decentralized distributed systems and the security must be furnished as end-to-end. Unfortunately, existing solutions are insufficient to deal with these problems like CORBA which is considered a one-to-one relationship only, or DSAW which deals with end-to-end security but without taking into account the possibility of changing information sensitivity during runtime. This thesis provides a proposed mechanism for enforcing security policies and dealing with distributed systems’ security weakness in term of the software perspective. The proposed solution utilised Aspect-Oriented Programming (AOP), to address security concerns during compilation and running time. The proposed solution is based on a decentralized distributed system that adopts the multi-hop concept to deal with different requested tasks. The proposed system focused on how to achieve high accuracy, data integrity and high efficiency of the distributed system in real time. This is done through modularising the most efficient security solutions, Access Control and Cryptography, by using Aspect-Oriented Programming language. The experiments’ results show the proposed solution overcomes the shortage of the existing solutions by fully integrating with the decentralized distributed system to achieve dynamic, high cooperation, high performance and end-to-end holistic security
Ethics - How to Protect Yourself & Preserve Confidentiality When Negotiating Instruments
Meeting proceedings of a seminar by the same name, held August 16, 2022
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