112 research outputs found
Copyright Protection of 3D Digitized Sculptures by Use of Haptic Device for Adding Local-Imperceptible Bumps
This research aims to improve some approaches for protecting digitized 3D models of cultural heritage objects such as the approach shown in the authors\u27 previous research on this topic. This technique can be used to protect works of art such as 3D models of sculptures, pottery, and 3D digital characters for animated film and gaming. It can also be used to preserve architectural heritage. In the research presented here adding protection to the scanned 3D model of the original sculpture was achieved using the digital sculpting technique with a haptic device. The original 3D model and the model with added protection were after that printed at the 3D printer, and then such 3D printed models were scanned. In order to measure the thickness of added protection, the original 3D model and the model with added protection were compared. Also, two scanned models of the printed sculptures were compared to define the amount of added material. The thickness of the added protection is up to 2 mm, whereas the highest difference detected between a matching scan of the original sculpture (or protected 3D model) and a scan of its printed version (or scan of the protected printed version) is about 1 mm
A survey of digital image watermarking techniques
Watermarking, which belong to the information hiding field, has seen a lot of research interest recently. There is a lot of work begin conducted in different branches in this field. Steganography is used for secret conmunication, whereas watermarking is used for content protection, copyright management, content authentication and tamper detection. In this paper we present a detailed survey of existing and newly proposed steganographic and watenmarking techniques. We classify the techniques based on different domains in which data is embedded. Here we limit the survey to images only
An Overview of Backdoor Attacks Against Deep Neural Networks and Possible Defences
Together with impressive advances touching every aspect of our society, AI technology based on Deep Neural Networks (DNN) is bringing increasing security concerns. While attacks operating at test time have monopolised the initial attention of researchers, backdoor attacks, exploiting the possibility of corrupting DNN models by interfering with the training process, represent a further serious threat undermining the dependability of AI techniques. In backdoor attacks, the attacker corrupts the training data to induce an erroneous behaviour at test time. Test-time errors, however, are activated only in the presence of a triggering event. In this way, the corrupted network continues to work as expected for regular inputs, and the malicious behaviour occurs only when the attacker decides to activate the backdoor hidden within the network. Recently, backdoor attacks have been an intense research domain focusing on both the development of new classes of attacks, and the proposal of possible countermeasures. The goal of this overview is to review the works published until now, classifying the different types of attacks and defences proposed so far. The classification guiding the analysis is based on the amount of control that the attacker has on the training process, and the capability of the defender to verify the integrity of the data used for training, and to monitor the operations of the DNN at training and test time. Hence, the proposed analysis is suited to highlight the strengths and weaknesses of both attacks and defences with reference to the application scenarios they are operating in
An Overview of Backdoor Attacks Against Deep Neural Networks and Possible Defences
Together with impressive advances touching every aspect of our society, AI technology based on Deep Neural Networks (DNN) is bringing increasing security concerns. While attacks operating at test time have monopolised the initial attention of researchers, backdoor attacks, exploiting the possibility of corrupting DNN models by interfering with the training process, represent a further serious threat undermining the dependability of AI techniques. In backdoor attacks, the attacker corrupts the training data to induce an erroneous behaviour at test time. Test-time errors, however, are activated only in the presence of a triggering event. In this way, the corrupted network continues to work as expected for regular inputs, and the malicious behaviour occurs only when the attacker decides to activate the backdoor hidden within the network. Recently, backdoor attacks have been an intense research domain focusing on both the development of new classes of attacks, and the proposal of possible countermeasures. The goal of this overview is to review the works published until now, classifying the different types of attacks and defences proposed so far. The classification guiding the analysis is based on the amount of control that the attacker has on the training process, and the capability of the defender to verify the integrity of the data used for training, and to monitor the operations of the DNN at training and test time. Hence, the proposed analysis is suited to highlight the strengths and weaknesses of both attacks and defences with reference to the application scenarios they are operating in
A Survey on LLM-generated Text Detection: Necessity, Methods, and Future Directions
The powerful ability to understand, follow, and generate complex language
emerging from large language models (LLMs) makes LLM-generated text flood many
areas of our daily lives at an incredible speed and is widely accepted by
humans. As LLMs continue to expand, there is an imperative need to develop
detectors that can detect LLM-generated text. This is crucial to mitigate
potential misuse of LLMs and safeguard realms like artistic expression and
social networks from harmful influence of LLM-generated content. The
LLM-generated text detection aims to discern if a piece of text was produced by
an LLM, which is essentially a binary classification task. The detector
techniques have witnessed notable advancements recently, propelled by
innovations in watermarking techniques, zero-shot methods, fine-turning LMs
methods, adversarial learning methods, LLMs as detectors, and human-assisted
methods. In this survey, we collate recent research breakthroughs in this area
and underscore the pressing need to bolster detector research. We also delve
into prevalent datasets, elucidating their limitations and developmental
requirements. Furthermore, we analyze various LLM-generated text detection
paradigms, shedding light on challenges like out-of-distribution problems,
potential attacks, and data ambiguity. Conclusively, we highlight interesting
directions for future research in LLM-generated text detection to advance the
implementation of responsible artificial intelligence (AI). Our aim with this
survey is to provide a clear and comprehensive introduction for newcomers while
also offering seasoned researchers a valuable update in the field of
LLM-generated text detection. The useful resources are publicly available at:
https://github.com/NLP2CT/LLM-generated-Text-Detection
Recent Advances in Signal Processing
The signal processing task is a very critical issue in the majority of new technological inventions and challenges in a variety of applications in both science and engineering fields. Classical signal processing techniques have largely worked with mathematical models that are linear, local, stationary, and Gaussian. They have always favored closed-form tractability over real-world accuracy. These constraints were imposed by the lack of powerful computing tools. During the last few decades, signal processing theories, developments, and applications have matured rapidly and now include tools from many areas of mathematics, computer science, physics, and engineering. This book is targeted primarily toward both students and researchers who want to be exposed to a wide variety of signal processing techniques and algorithms. It includes 27 chapters that can be categorized into five different areas depending on the application at hand. These five categories are ordered to address image processing, speech processing, communication systems, time-series analysis, and educational packages respectively. The book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity
A domain adaptive deep learning solution for scanpath prediction of paintings
Cultural heritage understanding and preservation is an important issue for
society as it represents a fundamental aspect of its identity. Paintings
represent a significant part of cultural heritage, and are the subject of study
continuously. However, the way viewers perceive paintings is strictly related
to the so-called HVS (Human Vision System) behaviour. This paper focuses on the
eye-movement analysis of viewers during the visual experience of a certain
number of paintings. In further details, we introduce a new approach to
predicting human visual attention, which impacts several cognitive functions
for humans, including the fundamental understanding of a scene, and then extend
it to painting images. The proposed new architecture ingests images and returns
scanpaths, a sequence of points featuring a high likelihood of catching
viewers' attention. We use an FCNN (Fully Convolutional Neural Network), in
which we exploit a differentiable channel-wise selection and Soft-Argmax
modules. We also incorporate learnable Gaussian distributions onto the network
bottleneck to simulate visual attention process bias in natural scene images.
Furthermore, to reduce the effect of shifts between different domains (i.e.
natural images, painting), we urge the model to learn unsupervised general
features from other domains using a gradient reversal classifier. The results
obtained by our model outperform existing state-of-the-art ones in terms of
accuracy and efficiency.Comment: Accepted at CBMI2022 graz, austri
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