by jsendak | Nov 14, 2024 | AI
arXiv:2411.08148v1 Announce Type: new
Abstract: Pioneering advancements in artificial intelligence, especially in genAI, have enabled significant possibilities for content creation, but also led to widespread misinformation and false content. The growing sophistication and realism of deepfakes is raising concerns about privacy invasion, identity theft, and has societal, business impacts, including reputational damage and financial loss. Many deepfake detectors have been developed to tackle this problem. Nevertheless, as for every AI model, the deepfake detectors face the wrath of lack of considerable generalization to unseen scenarios and cross-domain deepfakes. Besides, adversarial robustness is another critical challenge, as detectors drastically underperform to the slightest imperceptible change. Most state-of-the-art detectors are trained on static datasets and lack the ability to adapt to emerging deepfake attack trends. These three crucial challenges though hold paramount importance for reliability in practise, particularly in the deepfake domain, are also the problems with any other AI application. This paper proposes an adversarial meta-learning algorithm using task-specific adaptive sample synthesis and consistency regularization, in a refinement phase. By focussing on the classifier’s strengths and weaknesses, it boosts both robustness and generalization of the model. Additionally, the paper introduces a hierarchical multi-agent retrieval-augmented generation workflow with a sample synthesis module to dynamically adapt the model to new data trends by generating custom deepfake samples. The paper further presents a framework integrating the meta-learning algorithm with the hierarchical multi-agent workflow, offering a holistic solution for enhancing generalization, robustness, and adaptability. Experimental results demonstrate the model’s consistent performance across various datasets, outperforming the models in comparison.
Expert Commentary: Advancements in deepfake detection and the need for generalization and robustness
Artificial intelligence has made significant advancements in the field of deepfake detection, but it has also brought about new challenges. This paper highlights three crucial challenges faced by deepfake detectors – lack of generalization to unseen scenarios and cross-domain deepfakes, adversarial robustness, and the inability to adapt to emerging attack trends. These challenges are not unique to the deepfake domain but exist in other AI applications as well.
The lack of generalization to unseen scenarios and cross-domain deepfakes is a significant concern. AI models trained on specific datasets often struggle to perform well on real-world scenarios that they have not encountered during training. This is because deepfakes are continually evolving and becoming more sophisticated, making it challenging for detectors to keep up. The proposed adversarial meta-learning algorithm addresses this issue by focusing on the strengths and weaknesses of the classifier and refining it to improve both robustness and generalization.
Adversarial robustness is another critical challenge. Deepfake detectors often fail to detect slight imperceptible changes in deepfakes, which can be exploited by attackers. Adversarial attacks aim to deceive detectors by introducing subtle modifications to the deepfake. The proposed algorithm tackles this challenge by incorporating consistency regularization, which helps the detector react consistently to adversarial changes, making it more robust.
Furthermore, the paper introduces a hierarchical multi-agent retrieval-augmented generation workflow. This workflow, combined with a sample synthesis module, allows the model to dynamically adapt to new data trends by generating custom deepfake samples. This addresses the challenge of adapting to emerging attack trends and ensures that the model stays up-to-date with the latest deepfake techniques.
The integration of the meta-learning algorithm with the hierarchical multi-agent workflow offers a holistic solution for enhancing generalization, robustness, and adaptability. By combining these techniques, the proposed framework demonstrates consistent performance across various datasets, surpassing other models in comparison.
This research highlights the multi-disciplinary nature of deepfake detection. It involves advancements in artificial intelligence, specifically genAI, and draws upon concepts from computer vision, machine learning, and adversarial attacks. The proposed framework provides valuable insights and solutions not only for the deepfake domain but also for other AI applications facing similar challenges.
In conclusion, while deepfake detection has come a long way, there is still much work to be done to improve generalization, robustness, and adaptability. The proposed framework presented in this paper offers a promising approach to tackle these challenges and lays the foundation for further advancements in deepfake detection and other AI applications.
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by jsendak | Nov 6, 2024 | AI
arXiv:2411.02468v1 Announce Type: new
Abstract: A Multi Robot System (MRS) is the infrastructure of an intelligent cyberphysical system, where the robots understand the need of the human, and hence cooperate together to fulfill this need. Modeling an MRS is a crucial aspect of designing the proper system architecture, because this model can be used to simulate and measure the performance of the proposed architecture. However, an MRS solution architecture modeling is a very difficult problem, as it contains many dependent behaviors that dynamically change due to the current status of the overall system. In this paper, we introduce a general purpose MRS case study, where the humans initiate requests that are achieved by the available robots. These requests require different plans that use the current capabilities of the available robots. After proposing an architecture that defines the solution components, three steps are followed. First is modeling these components via Business Process Model and Notation (BPMN) language. BPMN provides a graphical notation to precisely represent the behaviors of every component, which is an essential need to model the solution. Second is to simulate these components behaviors and interaction in form of software agents. Java Agent DEvelopment (JADE) middleware has been used to develop and simulate the proposed model. JADE is based on a reactive agent approach, therefore it can dynamically represent the interaction among the solution components. Finally is to analyze the performance of the solution by defining a number of quantitative measurements, which can be obtained while simulating the system model in JADE middleware, therefore the solution can be analyzed and compared to another architecture.
Analysis: Modeling Multi Robot Systems for Intelligent Cyberphysical Systems
The concept of Multi Robot Systems (MRS) is a crucial aspect of designing intelligent cyberphysical systems, where robots cooperate to fulfill the needs of humans. Modeling an MRS is a complex task, as it involves understanding and simulating the dynamic behaviors and interactions among the robots and humans within the system. This paper presents a general purpose MRS case study and proposes an architecture for modeling and simulating MRS solutions.
Multi-disciplinary Nature
The study of MRS and intelligent cyberphysical systems is inherently multi-disciplinary, combining concepts from robotics, artificial intelligence, human-computer interaction, and system architecture design. Understanding the needs of humans and developing robots that can effectively cooperate to fulfill those needs requires expertise in these diverse fields.
Architecture Design for MRS
The paper proposes an architecture for MRS solution modeling, which defines the components necessary for achieving the requested tasks. By modeling the components using the Business Process Model and Notation (BPMN) language, the behaviors of each component can be precisely represented. This is a crucial step in developing an accurate simulation of the system.
Simulation using JADE Middleware
To simulate the behaviors and interactions of the MRS solution components, the paper utilizes Java Agent DEvelopment (JADE) middleware. JADE is based on a reactive agent approach, which allows for dynamic representation of the interactions among the components. This enables a more realistic simulation of the MRS system.
Performance Analysis
In order to evaluate the proposed MRS architecture, the paper suggests analyzing the performance of the solution. This is done by defining quantitative measurements that can be obtained while simulating the system model using the JADE middleware. By comparing the performance of different architectures, the effectiveness and efficiency of the proposed solution can be assessed.
Expert Insights
The study of Multi Robot Systems is essential for advancing the field of intelligent cyberphysical systems. By developing models and architectures that accurately represent the behaviors and interactions of the system components, researchers and engineers can better understand and optimize the performance of these systems.
This paper highlights the multi-disciplinary nature of MRS research, as it requires expertise from various fields to develop effective solutions. Robotics, artificial intelligence, human-computer interaction, and system architecture design all play a role in the development of MRS architectures.
The use of BPMN language for modeling the solution components provides a standardized and precise representation of behaviors. This allows for more accurate simulations and measurements of system performance.
The choice of JADE middleware for simulating the system is a suitable one, as it supports the dynamic representation of component interactions. This enhances the realism of the simulation and allows for more realistic analysis of the proposed MRS architecture.
Overall, this paper provides valuable insights into the modeling and simulation of Multi Robot Systems for intelligent cyberphysical systems. Further research in this area can build upon the proposed architecture and methodologies to develop more sophisticated and efficient MRS solutions.
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by jsendak | Oct 31, 2024 | Computer Science
arXiv:2410.22350v1 Announce Type: new
Abstract: In this paper, we propose a quality-aware end-to-end audio-visual neural speaker diarization framework, which comprises three key techniques. First, our audio-visual model takes both audio and visual features as inputs, utilizing a series of binary classification output layers to simultaneously identify the activities of all speakers. This end-to-end framework is meticulously designed to effectively handle situations of overlapping speech, providing accurate discrimination between speech and non-speech segments through the utilization of multi-modal information. Next, we employ a quality-aware audio-visual fusion structure to address signal quality issues for both audio degradations, such as noise, reverberation and other distortions, and video degradations, such as occlusions, off-screen speakers, or unreliable detection. Finally, a cross attention mechanism applied to multi-speaker embedding empowers the network to handle scenarios with varying numbers of speakers. Our experimental results, obtained from various data sets, demonstrate the robustness of our proposed techniques in diverse acoustic environments. Even in scenarios with severely degraded video quality, our system attains performance levels comparable to the best available audio-visual systems.
Expert Commentary: A Quality-Aware End-to-End Audio-Visual Neural Speaker Diarization Framework
This paper presents a novel approach to audio-visual speaker diarization, which is the process of determining who is speaking when in an audio or video recording. Speaker diarization is a crucial step in various multimedia information systems, such as video conferencing, surveillance systems, and automatic transcription services. This research proposes a quality-aware end-to-end framework that leverages both audio and visual information to accurately identify and separate individual speakers, even in challenging scenarios.
The proposed framework is multi-disciplinary in nature, combining concepts from audio processing, computer vision, and deep learning. By taking both audio and visual features as inputs, the model is able to capture a broader range of information, leading to more accurate speaker discrimination. This multi-modal approach allows the system to handle situations with overlapping speech, where audio-only methods may struggle.
One key aspect of this framework is the quality-aware audio-visual fusion structure. It addresses signal quality issues that commonly arise in real-world scenarios, such as noise, reverberation, occlusions, and unreliable detection. By incorporating quality-aware fusion, the system can mitigate the negative effects of audio and video degradations, leading to more robust performance. This is particularly important in applications where the video quality may be compromised, as the proposed framework can still perform at high levels.
Another notable contribution of this research is the use of a cross attention mechanism applied to multi-speaker embedding. This mechanism enables the network to handle scenarios with varying numbers of speakers. This is crucial in real-world scenarios where the number of speakers may change dynamically, such as meetings or group conversations.
The experimental results presented in the paper demonstrate the effectiveness and robustness of the proposed techniques. The framework achieves competitive performance on various datasets, even in situations with severely degraded video quality. These results highlight the potential of leveraging both audio and visual information for speaker diarization tasks.
In the wider field of multimedia information systems, this research contributes to the advancement of audio-visual processing techniques. By combining audio and visual cues, the proposed framework enhances the capabilities of multimedia systems, enabling more accurate and reliable speaker diarization. This has implications for various applications, including video surveillance, automatic transcription services, and virtual reality systems.
Furthermore, the concepts presented in this paper have connections to other related fields such as animations, artificial reality, augmented reality, and virtual realities. The use of audio-visual fusion and multi-modal information processing can be applied to enhance user experiences in these domains. For example, in virtual reality, accurate audio-visual synchronization and speaker separation can greatly enhance the immersion and realism of virtual environments, leading to more engaging experiences for users.
In conclusion, this paper introduces a quality-aware end-to-end audio-visual neural speaker diarization framework that leverages multi-modal information and addresses signal quality issues. The proposed techniques demonstrate robust performance in diverse acoustic environments, highlighting the potential of combining audio and visual cues for speaker diarization tasks. This research contributes to the wider field of multimedia information systems and has implications for various related domains, such as animations, artificial reality, augmented reality, and virtual realities.
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by jsendak | Oct 30, 2024 | AI
To prevent unauthorized use of text in images, Scene Text Removal (STR) has become a crucial task. It focuses on automatically removing text and replacing it with a natural, text-less background…
In today’s digital age, the unauthorized use of text in images has become a widespread concern. To combat this issue, a revolutionary technique called Scene Text Removal (STR) has emerged as a crucial task. STR aims to automatically remove text from images and replace it with a seamless, text-less background, ensuring the integrity and privacy of visual content. This article delves into the core themes of STR, exploring its significance in preventing unauthorized use of text in images and highlighting its ability to restore images to their natural, text-free state.
Exploring Innovative Solutions and Ideas in Scene Text Removal (STR)
In today’s digital age, the presence of text in images has become ubiquitous. From advertisements to social media posts, text is an integral part of our visual culture. However, there are instances where the presence of text may be unwanted or burdensome, such as when manipulating images or creating a text-less background for aesthetic or privacy purposes. This is where Scene Text Removal (STR) comes into play.
The Crucial Task of Scene Text Removal
Scene Text Removal (STR) is a computational task that aims to automatically detect and remove text from images, replacing it with a natural, text-less background. Whether it is removing captions from images for further analysis or eliminating text for enhancing image aesthetics, STR has become an essential tool in various fields, including computer vision, image editing, and content moderation.
Understanding the Underlying Themes and Concepts
At its core, STR involves two fundamental themes: text detection and text inpainting. Text detection focuses on identifying and localizing text within an image, while text inpainting deals with replacing the detected text regions with meaningful visual content that blends seamlessly with the surrounding background.
Proposing Innovative Solutions for Scene Text Removal
As the field of STR evolves, researchers and developers continually propose innovative solutions to enhance the accuracy and efficiency of the techniques involved. One such idea is the integration of deep learning algorithms, specifically Convolutional Neural Networks (CNNs), for text detection and inpainting tasks.
Deep Learning and Text Detection
Deep learning models, particularly CNNs, have demonstrated remarkable performance in text detection tasks. By training CNNs on large datasets containing labeled images with and without text, these models can learn to differentiate between text and non-text regions, achieving impressive accuracy in identifying text within images.
Enhancing Text Inpainting with Generative Adversarial Networks (GANs)
In the realm of text inpainting, Generative Adversarial Networks (GANs) have shown promising results. GANs consist of two components: a generator network, responsible for creating plausible inpainting proposals, and a discriminator network, which evaluates the quality of the generated proposals.
By training GANs on paired datasets, consisting of images with text and their corresponding text-less versions, the generator network can learn to generate realistic inpainting proposals that seamlessly replace the text regions. Meanwhile, the discriminator network helps improve the realism and coherence of the generated proposals by providing feedback during the training process. This approach has the potential to create highly convincing text-free backgrounds while preserving the overall image context.
Conclusion
As Scene Text Removal (STR) becomes increasingly important in our digital landscape, innovative solutions like deep learning algorithms and GANs offer promising avenues for enhancing the accuracy and efficiency of text detection and inpainting tasks. These advancements open up new possibilities for both researchers and practitioners in various fields, enabling them to unlock the full potential of text removal and accompanying image manipulation techniques. By pushing the boundaries of STR, we can harness the power of visual content while seamlessly integrating it into our ever-evolving digital world.
Scene Text Removal (STR) is indeed a critical task in the field of computer vision, as it addresses the challenge of removing text from images. With the increasing prevalence of text in images, such as street signs, billboards, and captions, the need for automated text removal techniques has become paramount.
The primary objective of STR is to automatically detect and remove text while preserving the underlying content and context of the image. This task involves several complex steps, including text detection, character recognition, and inpainting.
Text detection algorithms play a crucial role in identifying the regions of an image that contain text. These algorithms utilize various techniques, such as edge detection, connected component analysis, and machine learning-based approaches, to accurately locate and segment text regions.
Once the text regions are identified, character recognition methods are employed to extract the textual content. Optical Character Recognition (OCR) techniques have made significant advancements in recent years, enabling accurate text extraction even in challenging scenarios involving complex fonts, distorted text, or low-resolution images.
After the text is recognized, the next step is to replace it with a text-less background seamlessly. This process, known as inpainting, aims to fill the void left by the removed text with plausible content that matches the surrounding context. Inpainting techniques leverage image synthesis and texture completion methods to generate visually coherent backgrounds.
Despite the advancements in STR, there are still several challenges that need to be addressed. One major hurdle is the removal of text from complex backgrounds, such as textures, patterns, or cluttered scenes. Text that overlaps with important objects or has similar colors to the background poses additional difficulties.
To overcome these challenges, researchers are exploring deep learning-based approaches, which have shown promising results in recent years. Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs) have demonstrated their effectiveness in text removal tasks by learning complex visual patterns and generating realistic background textures.
Looking ahead, we can expect further improvements in STR techniques driven by advancements in deep learning architectures, larger annotated datasets, and the integration of contextual information. Additionally, the development of real-time STR algorithms will be crucial for applications such as video editing, surveillance, and augmented reality.
Furthermore, the application of STR extends beyond text removal. It can also be utilized for text manipulation, where text is modified or replaced with different content, opening up possibilities for content editing, language translation, and image enhancement.
In conclusion, Scene Text Removal is an evolving field with immense potential. As technology progresses, we can anticipate more accurate and efficient STR algorithms that will enhance our ability to automatically remove text from images while preserving the visual integrity and context of the underlying content.
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by jsendak | Oct 28, 2024 | Computer Science
arXiv:2410.19459v1 Announce Type: new
Abstract: Neural Radiance Fields (NeRF) have revolutionized the field of 3D visual representation by enabling highly realistic and detailed scene reconstructions from a sparse set of images. NeRF uses a volumetric functional representation that maps 3D points to their corresponding colors and opacities, allowing for photorealistic view synthesis from arbitrary viewpoints. Despite its advancements, the efficient streaming of NeRF content remains a significant challenge due to the large amount of data involved. This paper investigates the rate-distortion performance of two NeRF streaming strategies: pixel-based and neural network (NN) parameter-based streaming. While in the former, images are coded and then transmitted throughout the network, in the latter, the respective NeRF model parameters are coded and transmitted instead. This work also highlights the trade-offs in complexity and performance, demonstrating that the NN parameter-based strategy generally offers superior efficiency, making it suitable for one-to-many streaming scenarios.
Neural Radiance Fields (NeRF) Streaming Strategies: A Closer Look
Neural Radiance Fields (NeRF) have revolutionized the field of 3D visual representation by enabling highly realistic and detailed scene reconstructions from a sparse set of images. This breakthrough has paved the way for photorealistic view synthesis from arbitrary viewpoints, opening up new possibilities in various domains such as virtual reality, augmented reality, and multimedia information systems.
However, one significant challenge that researchers and practitioners face is the efficient streaming of NeRF content. The large amount of data involved in representing these highly detailed scenes poses a daunting task in terms of transmission and rendering in real-time scenarios. To address this challenge, a recent paper investigates the rate-distortion performance of two NeRF streaming strategies: pixel-based and neural network (NN) parameter-based streaming.
The first strategy, pixel-based streaming, involves coding and transmitting images throughout the network. This approach allows for more straightforward encoding and decoding but requires a large amount of data to be transmitted, leading to potential bandwidth limitations and increased latency.
On the other hand, the second strategy, NN parameter-based streaming, focuses on coding and transmitting the respective NeRF model parameters instead of the images themselves. This approach offers a more efficient alternative as it reduces the amount of data that needs to be transmitted. By leveraging the learned parameters of the neural network, the reconstruction process can be performed on the receiver’s end, resulting in higher efficiency and lower bandwidth requirements.
The paper’s findings highlight the trade-offs between complexity and performance when comparing the two streaming strategies. In general, the NN parameter-based strategy offers superior efficiency and reduced data transmission requirements, making it particularly suitable for one-to-many streaming scenarios. This finding is crucial in the context of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities, where real-time rendering and transmission of complex scenes are essential.
The multi-disciplinary nature of the concepts explored in this work is evident. It combines techniques from computer graphics, machine learning, image and video coding, and multimedia systems to address the challenges of streaming NeRF content efficiently. By leveraging neural network architectures and understanding the interplay between the volumetric representation of scenes and data transmission, researchers can further enhance the realism and accessibility of complex 3D visualizations.
In conclusion, the study of streaming strategies for Neural Radiance Fields (NeRF) opens up exciting possibilities in the field of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. The findings of this paper shed light on the trade-offs and efficiencies of different approaches, allowing for improved real-time rendering and transmission of highly detailed 3D scenes. As researchers continue to delve into the multi-disciplinary aspects of this field, we can expect further advancements in the quality and accessibility of virtual visual experiences.
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