185 research outputs found
A Survey on Biometrics based Digital Image Watermarking Techniques and Applications
The improvements in Internet technologies and growing demands on online multimedia businesses have made digital copyrighting as a major challenge for businesses that are associated with online content distribution via diverse business models including pay-per-view subscription trading etc Copyright protection and the evidence for rightful ownership are major issues associated with the distribution of any digital images Digital watermarking is a probable solution for digital content owners that offer security to the digital content In recent years digital watermarking plays a vital role in providing the apposite solution and numerous researches have been carried out In this paper an extensive review of the prevailing literature related to the Bio- watermarking is presented together with classification by utilizing an assortment of techniques In addition a terse introduction about the Digital Watermarking is presented to get acquainted with the vital information on the subject of Digital Watermarkin
Data Hiding and Its Applications
Data hiding techniques have been widely used to provide copyright protection, data integrity, covert communication, non-repudiation, and authentication, among other applications. In the context of the increased dissemination and distribution of multimedia content over the internet, data hiding methods, such as digital watermarking and steganography, are becoming increasingly relevant in providing multimedia security. The goal of this book is to focus on the improvement of data hiding algorithms and their different applications (both traditional and emerging), bringing together researchers and practitioners from different research fields, including data hiding, signal processing, cryptography, and information theory, among others
Challenges and Remedies to Privacy and Security in AIGC: Exploring the Potential of Privacy Computing, Blockchain, and Beyond
Artificial Intelligence Generated Content (AIGC) is one of the latest
achievements in AI development. The content generated by related applications,
such as text, images and audio, has sparked a heated discussion. Various
derived AIGC applications are also gradually entering all walks of life,
bringing unimaginable impact to people's daily lives. However, the rapid
development of such generative tools has also raised concerns about privacy and
security issues, and even copyright issues in AIGC. We note that advanced
technologies such as blockchain and privacy computing can be combined with AIGC
tools, but no work has yet been done to investigate their relevance and
prospect in a systematic and detailed way. Therefore it is necessary to
investigate how they can be used to protect the privacy and security of data in
AIGC by fully exploring the aforementioned technologies. In this paper, we
first systematically review the concept, classification and underlying
technologies of AIGC. Then, we discuss the privacy and security challenges
faced by AIGC from multiple perspectives and purposefully list the
countermeasures that currently exist. We hope our survey will help researchers
and industry to build a more secure and robust AIGC system.Comment: 43 pages, 10 figure
Information Forensics and Security: A quarter-century-long journey
Information forensics and security (IFS) is an active R&D area whose goal is to ensure that people use devices, data, and intellectual properties for authorized purposes and to facilitate the gathering of solid evidence to hold perpetrators accountable. For over a quarter century, since the 1990s, the IFS research area has grown tremendously to address the societal needs of the digital information era. The IEEE Signal Processing Society (SPS) has emerged as an important hub and leader in this area, and this article celebrates some landmark technical contributions. In particular, we highlight the major technological advances by the research community in some selected focus areas in the field during the past 25 years and present future trends
A Study on Visually Encrypted Images for Rights Protection and Authentication
首都大学東京, 2014-03-25, 博士(工学), 甲第444号首都大学東
A Survey on ChatGPT: AI-Generated Contents, Challenges, and Solutions
With the widespread use of large artificial intelligence (AI) models such as
ChatGPT, AI-generated content (AIGC) has garnered increasing attention and is
leading a paradigm shift in content creation and knowledge representation. AIGC
uses generative large AI algorithms to assist or replace humans in creating
massive, high-quality, and human-like content at a faster pace and lower cost,
based on user-provided prompts. Despite the recent significant progress in
AIGC, security, privacy, ethical, and legal challenges still need to be
addressed. This paper presents an in-depth survey of working principles,
security and privacy threats, state-of-the-art solutions, and future challenges
of the AIGC paradigm. Specifically, we first explore the enabling technologies,
general architecture of AIGC, and discuss its working modes and key
characteristics. Then, we investigate the taxonomy of security and privacy
threats to AIGC and highlight the ethical and societal implications of GPT and
AIGC technologies. Furthermore, we review the state-of-the-art AIGC
watermarking approaches for regulatable AIGC paradigms regarding the AIGC model
and its produced content. Finally, we identify future challenges and open
research directions related to AIGC.Comment: 20 pages, 6 figures, 4 table
On the Robustness of Dataset Inference
Machine learning (ML) models are costly to train as they can require a
significant amount of data, computational resources and technical expertise.
Thus, they constitute valuable intellectual property that needs protection from
adversaries wanting to steal them. Ownership verification techniques allow the
victims of model stealing attacks to demonstrate that a suspect model was in
fact stolen from theirs.
Although a number of ownership verification techniques based on watermarking
or fingerprinting have been proposed, most of them fall short either in terms
of security guarantees (well-equipped adversaries can evade verification) or
computational cost. A fingerprinting technique, Dataset Inference (DI), has
been shown to offer better robustness and efficiency than prior methods.
The authors of DI provided a correctness proof for linear (suspect) models.
However, in a subspace of the same setting, we prove that DI suffers from high
false positives (FPs) -- it can incorrectly identify an independent model
trained with non-overlapping data from the same distribution as stolen. We
further prove that DI also triggers FPs in realistic, non-linear suspect
models. We then confirm empirically that DI in the black-box setting leads to
FPs, with high confidence.
Second, we show that DI also suffers from false negatives (FNs) -- an
adversary can fool DI (at the cost of incurring some accuracy loss) by
regularising a stolen model's decision boundaries using adversarial training,
thereby leading to an FN. To this end, we demonstrate that black-box DI fails
to identify a model adversarially trained from a stolen dataset -- the setting
where DI is the hardest to evade.
Finally, we discuss the implications of our findings, the viability of
fingerprinting-based ownership verification in general, and suggest directions
for future work.Comment: 19 pages; Accepted to Transactions on Machine Learning Research
06/202
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