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