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Deepfake News Detection: A Multimodal Framework Integrating LipNet, DeepSpeech and ResNET for Enhanced Audio-Visual Analysis
Ameena Khan, Muhammad Ahsan Aziz, Muhammad Junaid Asif, Naeem Akhter, Rana Fayyaz Ahmad
https://arxiv.org/abs/2607.20579 https://arxiv.org/pdf/2607.20579 https://arxiv.org/html/2607.20579
arXiv:2607.20579v1 Announce Type: new
Abstract: Deepfake news refers to AI-generated (or AI ma-nipulated) multimedia content intentionally generated to deceive audiences by manipulating the facial expressions, or speech while maintaining the realistic appearance. The rapid progress of generative AI has made the synthesis of highly realistic fake videos and cloned voices widely accessible, posing a serious threat to the authenticity of digital news media. This paper presents a multi-modal framework that discerns the authenticity of video content by jointly exploiting audio and visual cues, thereby addressing the challenge of detecting the deepfake videos. We proposed a framework that involves features extraction from lip movements, audio content and video frames. Lip movements and speech content are encoded using the LipNet and DeepSpeech2 models, while facial features are extracted by leveraging the use of BlazeFace and represented with ResNet18. The extracted feature vectors are concatenated into a holistic video representation and classified with an ensemble of machine learning and deep learning models, including Random Forest (RF), Multi-layer Perceptron (MLP) and Long Short-Term Memory (LSTM) networks. Exten-sive experiments performed on the FakeAVCeleb dataset shows that the proposed approach attains an accuracy of 94% using augmented audio features, outperforming a state-of-the-art multi-modal ensemble baseline. The results confirm the robustness and practical potential of the proposed framework for deepfake news detection.
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Interactive Extraction of High-Frequency Aesthetically-Coherent Colormaps
Santiago V. Lombeyda, Mathieu Desbrun
https://arxiv.org/abs/2607.26025 https://arxiv.org/pdf/2607.26025 https://arxiv.org/html/2607.26025
arXiv:2607.26025v1 Announce Type: new
Abstract: Color transfer functions (i.e. colormaps) exhibiting a high frequency luminosity component have proven to be useful in the visualization of data where feature detection or iso-contours recognition is essential. Having these colormaps also display a wide range of color and an aesthetically pleasing composition holds the potential to further aid image understanding and analysis. However producing such colormaps in an efficient manner with current colormap creation tools is difficult. We hereby demonstrate an interactive technique for extracting colormaps from artwork and pictures. We show how the rich and careful color design and dynamic luminance range of an existing image can be gracefully captured in a colormap and be utilized effectively in the exploration of complex datasets
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