The widespread emergence of face-swap Deepfake videos poses growing risks to digital security, privacy, and media integrity, necessitating effective forensic tools for identifying the source of such manipulations. Alt...
The widespread emergence of face-swap Deepfake videos poses growing risks to digital security, privacy, and media integrity, necessitating effective forensic tools for identifying the source of such manipulations. Although most prior research has focused primarily on binary Deepfake detection, the task of model attribution determining which generative model produced a given Deepfake remains underexplored. In this paper, we introduce FAME (Fake Attribution via Multilevel Embeddings), a lightweight and efficient spatio-temporal framework designed to capture subtle generative artifacts specific to different face-swap models. FAME integrates spatial and temporal attention mechanisms to improve attribution accuracy while remaining computationally efficient. We evaluate our model on three challenging and diverse datasets, which include Deepfake Detection and Manipulation (DFDM), FaceForensics++ (FF++), and FakeAVCeleb (FAVCeleb). The evaluation results show that FAME consistently performs better than existing methods in both accuracy and runtime, highlighting its potential for deployment in real-world forensic and information security applications. The code and pretrained models will be made publicly available at: https://***/wasim004/FAME/ .
This paper introduces a logic with a class of social network models that is based on standard Linear Temporal Logic (LTL), leveraging the power of existing model checkers for the analysis of social networks. We provid...
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Structural global parameter identifiability indicates whether one can determine a parameter’s value from given inputs and outputs in the absence of noise. If a given model has parameters for which there may be infini...
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To emulate human emotions in robots, the mathematical representation of emotion is important for each component of affective computing, such as emotion recognition, generation, and expression. In a method that learns ...
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This perspective paper positions computerscience education (CSed) equity pedagogies within a critical-sociocultural and humanizing theoretical framework with justice-centered implications for curriculum and learning ...
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ISBN:
(数字)9798350328325
ISBN:
(纸本)9798350328332
This perspective paper positions computerscience education (CSed) equity pedagogies within a critical-sociocultural and humanizing theoretical framework with justice-centered implications for curriculum and learning environments, specifically for Latinx youth. Our perspective is grounded in our ongoing research on the Remezcla Project, a culturally relevant CS informal-learning program that combines coding, music production, and identity exploration with Latinx youth. First, we contextualize identity within the guiding theories. Next, we describe practical applications found in the Remezcla Project’s pedagogical and curricular design. We conclude with the assertion that critically informed approaches can shift CSed in equitable and transformative ways.
The efficacy of content-based image classification is dependent on the richness of the feature vectors extracted from the image data. Traditional feature extraction techniques highlight single low level image characte...
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A future networking design called "software-defined networking" combines network programmability with centralized administration (SDN). Network administration is currently handled by SDN, which, at the regul...
A future networking design called "software-defined networking" combines network programmability with centralized administration (SDN). Network administration is currently handled by SDN, which, at the regulator, which acts as the cable network processor and divides the data plane into a control plane and a data aircraft. Recent breakthroughs in artificial intelligence (Al) have shown a stronger inclination for the science community to benefit from their ability to give learning capabilities and enhance Indicator. The author of this research provides a comprehensive analysis of initiatives underway to incorporate Al with SDN. The study concluded that the three primary Al thread where scientific research was centered were computerscience, conceptual, and fuzzy reasoning systems. The authors of this paper explore the several fields in which these approaches may be used, their potential future applications, and the innovations made possible by the integration of AI-based methods into the SDN paradigm. By choosing the right SDN controller, any large organization may lower network complexity, implementation expenses, and maintenance costs. The focus of this article is software defined networking's benefits and downsides (SDN). Following is a basic explanation of artificial intelligence and a list of some of its most significant applications in SDN.
Molecular sciences address a wide range of problems involving molecules of different types and sizes and their complexes. Recently, geometric deep learning, especially Graph Neural Networks (GNNs), has shown promising...
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In one-dimensional quantum emitter systems, the dynamics of atomic excitations are influenced by the collective coupling between emitters through photon-mediated dipole-dipole interactions. By introducing positional d...
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Consider the problem of minimizing an expected logarithmic loss over either the probability simplex or the set of quantum density matrices. This problem includes tasks such as solving the Poisson inverse problem, comp...
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