by jsendak | Aug 21, 2025 | Computer Science
arXiv:2508.14581v1 Announce Type: new
Abstract: FakeHunter is a multimodal deepfake detection framework that combines memory-guided retrieval, chain-of-thought (Observation-Thought-Action) reasoning, and tool-augmented verification to provide accurate and interpretable video forensics. FakeHunter encodes visual content using CLIP and audio using CLAP, generating joint audio-visual embeddings that retrieve semantically similar real exemplars from a FAISS-indexed memory bank for contextual grounding. Guided by the retrieved context, the system iteratively reasons over evidence to localize manipulations and explain them. When confidence is low, it automatically invokes specialized tools-such as zoom-in image forensics or mel-spectrogram inspection-for fine-grained verification. Built on Qwen2.5-Omni-7B, FakeHunter produces structured JSON verdicts that specify what was modified, where it occurs, and why it is judged fake. We also introduce X-AVFake, a benchmark comprising 5.7k+ manipulated and real videos (950+ min) annotated with manipulation type, region/entity, violated reasoning category, and free-form justification. On X-AVFake, FakeHunter achieves an accuracy of 34.75%, outperforming the vanilla Qwen2.5-Omni-7B by 16.87 percentage points and MiniCPM-2.6 by 25.56 percentage points. Ablation studies reveal that memory retrieval contributes a 7.75 percentage point gain, and tool-based inspection improves low-confidence cases to 46.50%. Despite its multi-stage design, the pipeline processes a 10-minute clip in 8 minutes on a single NVIDIA A800 (0.8x real-time) or 2 minutes on four GPUs (0.2x), demonstrating practical deployability.
FakeHunter: A Multimodal Deepfake Detection Framework
The article discusses the development of FakeHunter, a multimodal deepfake detection framework that utilizes a combination of memory-guided retrieval, chain-of-thought reasoning, and tool-augmented verification to enhance video forensics. This framework represents an innovative approach to combating the proliferation of deepfake content, which has increasingly become a significant concern in the era of digital manipulation.
One of the key aspects of FakeHunter is its integration of CLIP for visual content encoding and CLAP for audio encoding, enabling the generation of joint audio-visual embeddings that facilitate the retrieval of semantically similar real exemplars for contextual grounding. This multi-disciplinary approach draws on techniques from computer vision, natural language processing, and machine learning to improve the accuracy and interpretability of deepfake detection.
Application in Multimedia Information Systems
FakeHunter’s utilization of memory-guided retrieval and chain-of-thought reasoning aligns with concepts commonly found in multimedia information systems, where the integration of multiple modalities such as text, images, and video is essential for effective data analysis and retrieval. By incorporating these principles, FakeHunter demonstrates the potential for advancing multimedia forensics and data verification techniques in a rapidly evolving digital landscape.
Connection to Artificial Reality, Augmented Reality, and Virtual Realities
The development of deepfake detection technologies like FakeHunter has significant implications for the fields of Artificial Reality, Augmented Reality, and Virtual Realities. As the boundaries between reality and digital fabrication continue to blur, the ability to accurately distinguish between authentic and manipulated content is crucial for preserving trust and credibility in virtual environments. FakeHunter’s use of specialized tools and iterative reasoning processes reflects a growing trend towards enhancing the authenticity and integrity of digital experiences.
In conclusion, FakeHunter represents a significant advancement in the field of deepfake detection, showcasing the potential of multi-disciplinary approaches to address complex challenges in digital media manipulation. By incorporating diverse techniques and methodologies, FakeHunter paves the way for future innovations in multimedia information systems, animations, artificial reality, augmented reality, and virtual realities.
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by jsendak | Aug 21, 2025 | AI
arXiv:2508.14214v1 Announce Type: new
Abstract: Emotions exert an immense influence over human behavior and cognition in both commonplace and high-stress tasks. Discussions of whether or how to integrate large language models (LLMs) into everyday life (e.g., acting as proxies for, or interacting with, human agents), should be informed by an understanding of how these tools evaluate emotionally loaded stimuli or situations. A model’s alignment with human behavior in these cases can inform the effectiveness of LLMs for certain roles or interactions. To help build this understanding, we elicited ratings from multiple popular LLMs for datasets of words and images that were previously rated for their emotional content by humans. We found that when performing the same rating tasks, GPT-4o responded very similarly to human participants across modalities, stimuli and most rating scales (r = 0.9 or higher in many cases). However, arousal ratings were less well aligned between human and LLM raters, while happiness ratings were most highly aligned. Overall LLMs aligned better within a five-category (happiness, anger, sadness, fear, disgust) emotion framework than within a two-dimensional (arousal and valence) organization. Finally, LLM ratings were substantially more homogenous than human ratings. Together these results begin to describe how LLM agents interpret emotional stimuli and highlight similarities and differences among biological and artificial intelligence in key behavioral domains.
Expert Commentary: Understanding Emotional Evaluation by Large Language Models
Emotions play a crucial role in human behavior and decision-making, influencing our interactions with the world around us. The integration of large language models (LLMs) into everyday life raises important questions about how these AI systems evaluate emotionally loaded stimuli and their alignment with human behavior.
It is essential to consider the multi-disciplinary nature of this topic, as it intersects psychology, artificial intelligence, and human-computer interaction. By conducting experiments that compare LLM ratings with human ratings for emotional content, researchers can gain insights into the effectiveness of these models in different roles and interactions.
The study mentioned in the article reveals interesting findings about the alignment between LLMs and humans in evaluating emotions. Notably, GPT-4o showed high similarity with human participants in rating emotional stimuli, particularly in happiness ratings. However, differences were observed in arousal ratings, indicating a level of discrepancy in the interpretation of emotionally charged content.
Furthermore, the study suggests that LLMs perform better within a categorical framework of emotions, such as happiness, anger, sadness, fear, and disgust, compared to a two-dimensional framework based on arousal and valence. This insight can inform future developments in AI systems designed to understand and respond to human emotions accurately.
Overall, the results of this research highlight the potential of LLMs in interpreting emotional stimuli and shed light on the behavioral differences between biological and artificial intelligence. Moving forward, further interdisciplinary studies and collaborations will be crucial in enhancing our understanding of how AI systems perceive and engage with emotions in human-computer interactions.
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by jsendak | Aug 21, 2025 | GR & QC Articles
arXiv:2508.14149v1 Announce Type: new
Abstract: We investigate the different meanings that the concept of Quantum Bounce acquires in various formalisms. The original idea refers to the phenomenology that appears in the Klein-Gordon framework when homogeneous cosmologies are considered. In that case, the Quantum Bounce describes the quantum scattering between a collapsing and an expanding Universe branch, and therefore provides a quantum description of the semiclassical Big Bounce mechanism. Here, we show that the proposal of the Quantum Big Bounce is well-grounded, thanks to the computation of the volume operator mean values and its standard deviation in the Wheeler-DeWitt framework for the isotropic case. Then, we analyze the Bianchi models in the Dirac approach, now showing that the Quantum Bounce concept can be implemented to describe the Kasner transitions of the Belinski-Khalatnikov-Lifshitz map at a quantum level. In summary, the quantum scattering framework borrowed from particle physics can serve as a good model for different cosmological scenarios, which can exhibit scalar-like or fermionic-like behaviours depending on how the anisotropies are described in the dynamics.
Conclusions
The concept of Quantum Bounce has different meanings across various formalisms, such as the Klein-Gordon framework and the Wheeler-DeWitt framework. In the context of homogeneous cosmologies, Quantum Bounce describes the quantum scattering between collapsing and expanding Universe branches, providing a quantum description of the semiclassical Big Bounce mechanism. The proposal of Quantum Big Bounce is supported by the computation of the volume operator mean values and standard deviation in the Wheeler-DeWitt framework for the isotropic case. Additionally, the application of the Quantum Bounce concept to Bianchi models in the Dirac approach allows for the description of Kasner transitions at a quantum level.
Future Roadmap
- Further investigate the implications of Quantum Bounce in different cosmological scenarios.
- Explore how anisotropies impact the scalar-like or fermionic-like behaviors observed in Quantum Bounce dynamics.
- Develop computational models to simulate and study Quantum Bounce phenomena in more complex cosmological systems.
- Collaborate with experimental physicists to devise potential tests or observations that could validate Quantum Bounce predictions.
Potential Challenges
- Complexity of mathematical formalisms and computational methods involved in studying Quantum Bounce.
- Interdisciplinary collaboration required to combine insights from quantum physics and cosmology.
- Lack of experimental data or observational evidence to verify Quantum Bounce predictions.
Opportunities on the Horizon
By leveraging advances in quantum physics and cosmology, the exploration of Quantum Bounce opens up new avenues for understanding the fundamental nature of the Universe’s evolution. The integration of theoretical insights with experimental observations could potentially revolutionize our understanding of cosmic phenomena and provide novel perspectives on the origins and fate of the cosmos.
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by jsendak | Aug 21, 2025 | Computer Science
Expert Commentary: A Closer Look at Sampling Kantorovich Operators
Sampling Kantorovich (SK) operators have been the subject of intense research in the field of image processing and approximation theory due to their ability to produce accurate results in the reconstruction of images. In this study, the convergence properties of positive sampling Kantorovich (SK) operators are examined, shedding light on their local and global approximation capabilities.
The researchers explore the use of SK operators alongside Gaussian, Bilateral, and thresholding wavelet-based operators to assess their effectiveness in image reconstruction. By defining the fundamental theorem of approximation (FTA) and imposing various conditions on the operators, they are able to measure errors and evaluate mathematical parameters such as mean square error (MSE), speckle index (SI), speckle suppression index (SSI), speckle mean preservation index (SMPI), and equivalent number of looks (ENL) at different levels of resolution.
An illustrative example demonstrates the nature of these operators under ideal conditions, showcasing their performance through tabulated results at a specific sample level. Furthermore, a numerical example involving the 2D Shepp-Logan Phantom image slice from a 3D image highlights the relevance of the operators in analyzing regions of interest (ROI) based on SI, SSI, and SMPI.
One key takeaway from this research is the acknowledgment that different operators exhibit varying levels of effectiveness in capturing specific image features due to the uneven nature of images under non-ideal conditions. This underscores the importance of selecting the appropriate operator for a given image analysis task, as not all operators may perform optimally across all scenarios.
By delving into the intricacies of sampling Kantorovich operators and their convergence properties, this study offers valuable insights into the nuances of image reconstruction and approximation techniques, paving the way for further advancements in the field of image processing.
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by jsendak | Aug 21, 2025 | Cosmology & Computing
Computing technology has come a long way since the invention of the first computer in the 1940s. From room-sized machines that could perform basic calculations to the sleek and powerful devices we carry in our pockets today, the evolution of computing has been nothing short of remarkable. But what lies ahead for the future of computing? One technology that has the potential to revolutionize the way we think about computing is quantum computing.
Quantum computing is a type of computing that uses quantum-mechanical phenomena, such as superposition and entanglement, to perform operations on data. Unlike classical computers, which use bits to represent information as either a 0 or a 1, quantum computers use quantum bits, or qubits, which can exist in a superposition of states. This allows quantum computers to perform calculations much faster than classical computers, potentially solving complex problems that are currently beyond the reach of even the most powerful supercomputers.
One of the key advantages of quantum computing is its ability to perform parallel computations. In a classical computer, each bit can only be in one state at a time, meaning that computations are performed sequentially. In a quantum computer, however, qubits can exist in multiple states simultaneously, allowing for the possibility of performing many calculations at once. This parallelism is what gives quantum computers their potential for exponential speedup over classical computers.
Another advantage of quantum computing is its ability to solve certain problems that are intractable for classical computers. For example, quantum computers have the potential to break encryption schemes that are currently considered secure, such as RSA encryption. This has significant implications for cybersecurity, as quantum computers could potentially render many of our current encryption methods obsolete.
Despite its potential, quantum computing is still in its early stages of development. Building a quantum computer is a complex and challenging task, requiring precise control over individual qubits and the ability to maintain their quantum states for long enough to perform calculations. Researchers are making progress in developing quantum computers, with companies like IBM, Google, and Microsoft investing heavily in the technology.
As quantum computing continues to advance, it has the potential to revolutionize fields such as cryptography, drug discovery, and optimization problems. Quantum computers could enable us to simulate complex molecules and materials, leading to breakthroughs in drug development and materials science. They could also revolutionize optimization problems, such as finding the most efficient route for delivery trucks or optimizing financial portfolios.
In conclusion, quantum computing holds great promise for the future of computing. While there are still many challenges to overcome, the potential benefits of quantum computing are too great to ignore. As researchers continue to make progress in developing quantum computers, we can expect to see quantum computing play an increasingly important role in shaping the future of technology.
