111 research outputs found
Security and Privacy on Generative Data in AIGC: A Survey
The advent of artificial intelligence-generated content (AIGC) represents a
pivotal moment in the evolution of information technology. With AIGC, it can be
effortless to generate high-quality data that is challenging for the public to
distinguish. Nevertheless, the proliferation of generative data across
cyberspace brings security and privacy issues, including privacy leakages of
individuals and media forgery for fraudulent purposes. Consequently, both
academia and industry begin to emphasize the trustworthiness of generative
data, successively providing a series of countermeasures for security and
privacy. In this survey, we systematically review the security and privacy on
generative data in AIGC, particularly for the first time analyzing them from
the perspective of information security properties. Specifically, we reveal the
successful experiences of state-of-the-art countermeasures in terms of the
foundational properties of privacy, controllability, authenticity, and
compliance, respectively. Finally, we summarize the open challenges and
potential exploration directions from each of theses properties
Scalable Wavelet-Based Active Network Stepping Stone Detection
Network intrusions leverage vulnerable hosts as stepping stones to penetrate deeper into a network and mask malicious actions from detection. This research focuses on a novel active watermark technique using Discrete Wavelet Transformations to mark and detect interactive network sessions. This technique is scalable, nearly invisible and resilient to multi-flow attacks. The watermark is simulated using extracted timestamps from the CAIDA 2009 dataset and replicated in a live environment. The simulation results demonstrate that the technique accurately detects the presence of a watermark at a 5% False Positive and False Negative rate for both the extracted timestamps as well as the empirical tcplib distribution. The watermark extraction accuracy is approximately 92%. The live experiment is implemented using the Amazon Elastic Compute Cloud. The client system sends marked and unmarked packets from California to Virginia using stepping stones in Tokyo, Ireland and Oregon. Five trials are conducted using simultaneous watermarked and unmarked samples. The live results are similar to the simulation and provide evidence demonstrating the effectiveness in a live environment to identify stepping stones
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
Prompting AI Art: An Investigation into the Creative Skill of Prompt Engineering
Humankind is entering a novel era of creativity - an era in which anybody can
synthesize digital content. The paradigm under which this revolution takes
place is prompt-based learning (or in-context learning). This paradigm has
found fruitful application in text-to-image generation where it is being used
to synthesize digital images from zero-shot text prompts in natural language
for the purpose of creating AI art. This activity is referred to as prompt
engineering - the practice of iteratively crafting prompts to generate and
improve images. In this paper, we investigate prompt engineering as a novel
creative skill for creating prompt-based art. In three studies with
participants recruited from a crowdsourcing platform, we explore whether
untrained participants could 1) recognize the quality of prompts, 2) write
prompts, and 3) improve their prompts. Our results indicate that participants
could assess the quality of prompts and respective images. This ability
increased with the participants' experience and interest in art. Participants
further were able to write prompts in rich descriptive language. However, even
though participants were specifically instructed to generate artworks,
participants' prompts were missing the specific vocabulary needed to apply a
certain style to the generated images. Our results suggest that prompt
engineering is a learned skill that requires expertise and practice. Based on
our findings and experience with running our studies with participants
recruited from a crowdsourcing platform, we provide ten recommendations for
conducting experimental research on text-to-image generation and prompt
engineering with a paid crowd. Our studies offer a deeper understanding of
prompt engineering thereby opening up avenues for research on the future of
prompt engineering. We conclude by speculating on four possible futures of
prompt engineering.Comment: 29 pages, 10 figure
Strategies for Unbridled Data Dissemination: An Emergency Operations Manual
This project is a study of free data dissemination and impediments to it. Drawing upon post-structuralism, Actor Network Theory, Participatory Action Research, and theories of the political stakes of the posthuman by way of Stirnerian egoism and illegalism, the project uses a number of theoretical, technical and legal texts to develop a hacker methodology that emphasizes close analysis and disassembly of existent systems of content control. Specifically, two tiers of content control mechanisms are examined: a legal tier, as exemplified by Intellectual Property Rights in the form of copyright and copyleft licenses, and a technical tier in the form of audio, video and text-based watermarking technologies.
A series of demonstrative case studies are conducted to further highlight various means of content distribution restriction. A close reading of a copyright notice is performed in order to examine its internal contradictions. Examples of watermarking employed by academic e-book and journal publishers and film distributors are also examined and counter-forensic techniques for removing such watermarks are developed. The project finds that both legal and technical mechanisms for restricting the flow of content can be countervailed, which in turn leads to the development of different control mechanisms and in turn engenders another wave of evasion procedures. The undertaken methodological approach thus leads to the discovery of on-going mutation and adaptation of in-between states of resistance.
Finally, an analysis of various existent filesharing applications is performed, and a new Tor-based BitTorrent tracker is set up to strengthen the anonymization of established filesharing methods. It is found that there exist potential de-anonymization attacks against all analyzed file-sharing tools, with potentially more secure filesharing options also seeing less user adoption
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