by jsendak | Dec 11, 2024 | Computer Science
Analysis of Large Language Models (LLMs) and Adversarial Attacks
Recent research has highlighted the vulnerabilities of large language models (LLMs) to adversarial attacks. This is a concerning finding, considering the widespread adoption of LLM-based chatbots and virtual assistants across various industries, fueled by the rapid development pace of AI-based systems.
The potential of Generative AI (GenAI) to assist humans in decision making is driving this development, sparking immense optimism. However, it is crucial to acknowledge and address the adversarial risks associated with these technologies.
An adversary exploiting security gaps, inadequate safeguards, and limited data governance can carry out attacks that grant unauthorized access to the system and its data. Such attacks can compromise the integrity, confidentiality, and availability of sensitive information.
Understanding Data Poison Attacks
As a means of demonstrating the potential vulnerabilities of LLM-based chatbots, a proof-of-concept assessment was conducted on BarkPlug, the chatbot developed by Mississippi State University.
The focus of this assessment was data poison attacks, a type of adversarial attack where the input data to the LLM is manipulated to manipulate the behavior of the chatbot. By injecting malicious or misleading information into the training data, an attacker can manipulate the responses generated by the chatbot.
By carefully crafting input that contains subtle but influential patterns, an adversary can deceive the chatbot into providing inaccurate or harmful information, leading to potential consequences for users relying on its responses.
Evaluating BarkPlug’s Performance
The proof-of-concept assessment aimed to evaluate BarkPlug’s resilience against data poison attacks. A red team perspective was adopted, mimicking the adversarial mindset to identify potential weaknesses.
The results of the assessment revealed vulnerabilities in BarkPlug’s ability to identify and respond to manipulated input. The chatbot exhibited a lack of robustness in distinguishing between genuine and maliciously crafted queries.
This finding is concerning, as it indicates the potential for attackers to exploit BarkPlug’s weaknesses to manipulate its responses and mislead users. In an environment where BarkPlug is utilized for decision making or information retrieval, such exploitation poses significant risks.
Addressing Adversarial Risks and Strengthening LLM Systems
The vulnerabilities identified in BarkPlug underscore the importance of addressing adversarial risks associated with LLM-based chatbots and virtual assistants.
There is a need for enhanced security measures, rigorous safeguards, and robust data governance to mitigate the risks of unauthorized access and manipulation of LLM systems.
Additionally, ongoing research and development in the field of adversarial machine learning are necessary to improve the resilience of LLMs against such attacks. Techniques such as adversarial training and data sanitization can help strengthen LLM systems.
Expert Insight: As LLM-based chatbots become more prevalent in various industries, it is crucial to strike a balance between harnessing the potential benefits of GenAI and addressing the inherent adversarial risks. By investing in security and resilience measures, organizations can ensure the trustworthiness and reliability of LLM systems.
Overall, this assessment sheds light on the vulnerabilities present in LLM-based chatbots and the importance of addressing adversarial risks to safeguard user trust and protect sensitive data. Continued research and proactive measures are essential in building robust LLM systems that can withstand adversarial attacks and maintain their effectiveness in decision-making processes.
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by jsendak | Dec 10, 2024 | Computer Science
arXiv:2412.05694v1 Announce Type: new
Abstract: This study presents a novel method for generating music visualisers using diffusion models, combining audio input with user-selected artwork. The process involves two main stages: image generation and video creation. First, music captioning and genre classification are performed, followed by the retrieval of artistic style descriptions. A diffusion model then generates images based on the user’s input image and the derived artistic style descriptions. The video generation stage utilises the same diffusion model to interpolate frames, controlled by audio energy vectors derived from key musical features of harmonics and percussives. The method demonstrates promising results across various genres, and a new metric, Audio-Visual Synchrony (AVS), is introduced to quantitatively evaluate the synchronisation between visual and audio elements. Comparative analysis shows significantly higher AVS values for videos generated using the proposed method with audio energy vectors, compared to linear interpolation. This approach has potential applications in diverse fields, including independent music video creation, film production, live music events, and enhancing audio-visual experiences in public spaces.
Music Visualizers: Blending Art and Technology
Music visualizers have long been used to enhance the auditory experience by adding a visual component to sound. This study presents a unique and innovative method for generating music visualizers using diffusion models, combining audio input with user-selected artwork. The multi-disciplinary nature of this concept lies in its integration of music analysis, art interpretation, and video generation techniques.
Image Generation and Artistic Style Descriptions
In the first stage of the process, music captioning and genre classification algorithms are employed to analyze the audio input. This analysis provides crucial information about the key musical features such as harmonics and percussives. Utilizing these features, artistic style descriptions are retrieved and combined with the user’s input image.
The diffusion model plays a central role in generating the images based on the user’s input and the artistic style descriptions. This technique allows for the creation of unique and visually stunning visuals that are in harmony with the music. The blending of audio and visual elements in this stage showcases the potential of this method to create immersive experiences.
Video Creation and Audio-Visual Synchrony
Once the images are generated, the same diffusion model is employed to interpolate frames and create a video. However, what sets this method apart is the use of audio energy vectors derived from the key musical features. These vectors control the interpolation, ensuring that the visual elements synchronize with the changes in audio energy.
The introduction of a new metric, Audio-Visual Synchrony (AVS), allows for a quantitative evaluation of the synchronisation between visual and audio elements. Comparative analysis has shown significantly higher AVS values for videos generated using the proposed method with audio energy vectors compared to linear interpolation. This indicates the effectiveness of this method in creating visually appealing and synchronized music visualizers.
Applications and Future Developments
The potential applications of this method are vast and span across various fields. Independent music video creators can use this technique to generate captivating visuals that complement their music. Film producers can incorporate this method in their productions to create unique and engaging visual experiences. Live music events can leverage this technology to enhance the audio-visual spectacle for the audience. Furthermore, this method can be applied in public spaces to create interactive and immersive audio-visual displays.
In relation to the wider field of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities, this study showcases the potential for integration of audio and visual elements in new and innovative ways. It highlights the important role that technology, such as diffusion models, can play in enhancing multimedia experiences. By bridging the gap between art and technology, this method paves the way for future developments in the field of music visualization and beyond.
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by jsendak | Dec 10, 2024 | Computer Science
Expert Commentary
The publication of the first International Scientific Report on the Safety of Advanced AI marks a significant milestone in the scientific community’s efforts to understand and manage the risks associated with general-purpose AI. With contributions from 75 AI experts, including an Expert Advisory Panel nominated by 30 countries, the EU, and the UN, this report represents a comprehensive synthesis of our current understanding of AI safety.
One of the key strengths of this report is its emphasis on a diverse range of perspectives. By involving experts from various countries and backgrounds, the report ensures a more holistic and global understanding of the risks posed by advanced AI. This approach is crucial, as AI technologies are rapidly advancing and being adopted in various sectors across the world.
The report’s focus on general-purpose AI is particularly noteworthy. While specialized AI systems have been widely studied, the potential risks and safety concerns associated with general-purpose AI are relatively less explored. By delving into this area, the report provides valuable insights into the unique challenges posed by AI systems that can perform a wide variety of tasks.
It is also important to highlight the independence and discretion given to the expert panel in shaping the report’s content. This ensures that the findings and recommendations are based on rigorous scientific analysis and not influenced by external interests. This approach strengthens the credibility and integrity of the report, making it a trusted resource for policymakers, researchers, and AI practitioners alike.
Looking ahead, this interim publication sets the stage for further research and collaboration in the field of AI safety. As AI technology continues to advance at an unprecedented pace, it is paramount that we stay ahead of potential risks and develop robust frameworks for managing them. The insights and recommendations presented in this report serve as a foundation for future discussions and actions in ensuring the safe and responsible development and deployment of AI.
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by jsendak | Dec 9, 2024 | Computer Science
arXiv:2412.04746v1 Announce Type: cross
Abstract: Modern music retrieval systems often rely on fixed representations of user preferences, limiting their ability to capture users’ diverse and uncertain retrieval needs. To address this limitation, we introduce Diff4Steer, a novel generative retrieval framework that employs lightweight diffusion models to synthesize diverse seed embeddings from user queries that represent potential directions for music exploration. Unlike deterministic methods that map user query to a single point in embedding space, Diff4Steer provides a statistical prior on the target modality (audio) for retrieval, effectively capturing the uncertainty and multi-faceted nature of user preferences. Furthermore, Diff4Steer can be steered by image or text inputs, enabling more flexible and controllable music discovery combined with nearest neighbor search. Our framework outperforms deterministic regression methods and LLM-based generative retrieval baseline in terms of retrieval and ranking metrics, demonstrating its effectiveness in capturing user preferences, leading to more diverse and relevant recommendations. Listening examples are available at tinyurl.com/diff4steer.
Diff4Steer: A Novel Generative Retrieval Framework for Music Exploration
Modern music retrieval systems often struggle to capture the diverse and uncertain retrieval needs of users. This limitation is due to their reliance on fixed representations of user preferences. To overcome this challenge, a team of researchers has introduced Diff4Steer, a highly innovative generative retrieval framework that aims to synthesize diverse seed embeddings from user queries, representing potential directions for music exploration.
Unlike deterministic methods that map user queries to a single point in embedding space, Diff4Steer employs lightweight diffusion models to provide a statistical prior on the target modality, which in this case is audio. This approach effectively captures the uncertainty and multi-faceted nature of user preferences, allowing for a more nuanced understanding of their musical tastes.
One of the standout features of Diff4Steer is its ability to be steered by image or text inputs, in addition to traditional audio queries. This unique functionality enables a more flexible and controllable music discovery experience, combined with advanced nearest neighbor search techniques. By incorporating different modalities, the framework allows users to explore music based on visual cues or textual descriptions, bridging the gap between different sensory experiences.
The use of diffusion models in Diff4Steer holds promise for the wider field of multimedia information systems. The concept of using statistical priors to capture uncertainty and leverage diverse data sources is not only relevant to music retrieval but can also be applied to other domains where unstructured multimedia data is prevalent. By expanding the scope of this framework beyond music, researchers and practitioners can explore its potential in analyzing and retrieving multimedia content such as images, videos, and text.
Furthermore, Diff4Steer’s integration of artificial reality, augmented reality, and virtual realities can enhance the music exploration experience. By incorporating these technologies, users can visualize and interact with music in immersive environments, adding a new layer of engagement and sensory stimulation. This multidisciplinary approach opens up avenues for cross-pollination between the fields of multimedia information systems and virtual reality, leading to the development of more immersive and interactive music retrieval systems.
In terms of performance, Diff4Steer demonstrates its effectiveness in capturing user preferences and generating more diverse and relevant recommendations. It outperforms deterministic regression methods and a generative retrieval baseline, showcasing the superiority of its statistical approach. By providing a wider range of music options to users, Diff4Steer has the potential to enhance music discovery and foster a deeper connection between listeners and their preferred genres.
In conclusion, Diff4Steer offers a groundbreaking solution to the limitations of traditional music retrieval systems. By incorporating lightweight diffusion models and the ability to be steered by different modalities, it provides a more comprehensive understanding of user preferences and enables a more flexible and controllable music exploration experience. Its implications extend beyond the field of music, opening up new possibilities in multimedia information systems, artificial reality, augmented reality, and virtual realities.
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by jsendak | Dec 9, 2024 | Computer Science
Network data packet anomaly detection is a critical task in ensuring the security and integrity of computer networks. However, it is a challenging problem due to several factors, such as the evolving nature of attacks and the increasing complexity of network traffic. In this article, we discuss a recent research paper that proposes a novel approach called NIDS-GPT for network intrusion detection.
The Innovation of NIDS-GPT
NIDS-GPT stands for Network Intrusion Detection System based on the Generative Pre-trained Transformer (GPT) model. Unlike previous approaches, NIDS-GPT treats each number in the network data packet as an independent “word” instead of considering the entire packet field. This fine-grained representation allows for a more detailed analysis of the data, capturing both the structure and semantics.
In order to implement NIDS-GPT, the researchers improve upon the existing GPT-2 model. They design special tokenizers and embedding layers to better understand the network data. By doing so, they enhance the model’s ability to detect anomalies in an unsupervised manner, which is crucial for real-world scenarios where labeled data may be limited.
Scalability and Model Interpretability
One of the key advantages of NIDS-GPT is its scalability. The model demonstrates good performance even in the face of extreme data imbalance, achieving 100% accuracy under such conditions. Traditional methods often struggle with imbalanced data, making this a noteworthy achievement.
Furthermore, NIDS-GPT offers improved model interpretability through attention weight visualization. This means that analysts can better understand the decisions made by the model and gain insights into the underlying patterns that contribute to anomaly detection.
Evaluation Results
The researchers conducted experiments on two datasets, namely CICIDS2017 and car-hacking datasets, to evaluate the performance of NIDS-GPT. The results were impressive, with the model achieving over 90% accuracy in one-shot learning and surpassing traditional methods in terms of overall accuracy.
Potential and Future Directions
The findings of this research paper indicate that NIDS-GPT has the potential to handle complex network anomaly detection tasks effectively. Its ability to handle data imbalance and resource-constrained scenarios makes it a promising solution for real-world applications.
In terms of future directions, further research can explore the integration of NIDS-GPT with other machine learning techniques to enhance its performance even further. Additionally, the adaptability of NIDS-GPT to different network architectures and protocols can be investigated to assess its applicability in diverse environments.
In conclusion, NIDS-GPT offers a novel approach to network intrusion detection by leveraging the power of GPT-based models. Its fine-grained data representation, scalability, and model interpretability make it a valuable tool in combating network anomalies. With further advancements and improvements, NIDS-GPT holds the potential to significantly strengthen the security of computer networks in the future.
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https://github.com/NIDS-GPT
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by jsendak | Dec 7, 2024 | Computer Science
arXiv:2412.04307v1 Announce Type: new
Abstract: Large models have achieved remarkable performance across various tasks, yet they incur significant computational costs and privacy concerns during both training and inference. Distributed deployment has emerged as a potential solution, but it necessitates the exchange of intermediate information between model segments, with feature representations serving as crucial information carriers. To optimize information exchange, feature coding methods are applied to reduce transmission and storage overhead. Despite its importance, feature coding for large models remains an under-explored area. In this paper, we draw attention to large model feature coding and make three contributions to this field. First, we introduce a comprehensive dataset encompassing diverse features generated by three representative types of large models. Second, we establish unified test conditions, enabling standardized evaluation pipelines and fair comparisons across future feature coding studies. Third, we introduce two baseline methods derived from widely used image coding techniques and benchmark their performance on the proposed dataset. These contributions aim to advance the field of feature coding, facilitating more efficient large model deployment. All source code and the dataset will be made available on GitHub.
Feature Coding for Large Models: Advancements in Efficient Deployment
In recent years, large models have shown exceptional performance across various tasks, but they come with inherent challenges such as high computational costs and privacy concerns. As a result, distributed deployment has emerged as a potential solution, allowing for the efficient utilization of resources while addressing privacy concerns. However, this method requires the exchange of intermediate information between model segments, making feature representations crucial carriers of information.
Feature coding plays a vital role in optimizing information exchange by reducing transmission and storage overhead. Despite its importance, feature coding for large models remains a relatively under-explored area. In this paper, we shed light on the significance of feature coding for large models and make three key contributions to this field.
Comprehensive Dataset and Unified Test Conditions
We begin by introducing a comprehensive dataset that encompasses diverse features generated by three representative types of large models. This dataset serves as a valuable resource for researchers and practitioners in understanding the characteristics and properties of features in large models.
We also establish unified test conditions, enabling standardized evaluation pipelines and fair comparisons across future feature coding studies. This standardization is essential in promoting reproducibility and ensuring that advancements in feature coding can be accurately assessed and benchmarked against existing approaches.
Baseline Methods and Performance Evaluation
To kickstart advancements in feature coding for large models, we introduce two baseline methods derived from widely used image coding techniques. These methods provide a starting point for researchers to explore and develop more sophisticated feature coding approaches.
We benchmark the performance of these baseline methods on the proposed comprehensive dataset, allowing for comparative analysis. Through this evaluation, we aim to provide insights into the strengths and limitations of existing feature coding techniques while paving the way for further enhancements.
Multi-Disciplinary Nature and Relation to Multimedia Information Systems
The concepts and advancements in feature coding for large models have a multi-disciplinary nature and are closely related to the wider field of multimedia information systems. Multimedia information systems deal with the processing, storage, retrieval, and transmission of multimedia data, including text, images, videos, and audio.
Large models, animations, artificial reality, augmented reality, and virtual realities are all integral components of multimedia information systems. Feature coding techniques play a crucial role in optimizing the transmission and storage of these diverse multimedia data, enabling more efficient and effective deployment of large models in various applications.
By addressing the challenges and limitations of feature coding for large models, we can unlock new possibilities for multimedia information systems, allowing for more seamless integration of advanced technologies and richer user experiences.
In summary, this paper highlights the significance of feature coding for large models and presents valuable contributions to this under-explored area. The introduced comprehensive dataset, unified test conditions, and baseline methods open doors for further research, development, and advancements in feature coding. The multi-disciplinary nature of these concepts reinforces their relation to multimedia information systems, expanding the horizons of animations, artificial reality, augmented reality, and virtual realities.
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