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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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A Positron Range Correction with Texture Preservation Framework in PET Imaging
Nerea Encina-Baranda, Yifan Zheng, Jorge Cabello, Robert. J. Paneque-Yunta, Cindy. M. Solano-Cordero, Alejandro Lopez-Montes, Maurizio Conti, Joaquin. L. Herraiz
https://arxiv.org/abs/2606.23100 https://arxiv.org/pdf/2606.23100 https://arxiv.org/html/2606.23100
arXiv:2606.23100v1 Announce Type: new
Abstract: Positron range (PR) blurring is a fundamental resolution limitation in PET imaging with high-energy positron emitters such as 82Rb, causing contrast loss and spill-out effects across heterogeneous tissue interfaces. We propose PRC-TP, a positron range correction (PRC) framework with explicit texture preservation that decouples deterministic resolution recovery from stochastic texture restoration. A nnFormer-based neural network (NN) was trained on patient-derived Monte Carlo simulations to map PR-degraded 82Rb reconstructions to PR-free references using attenuation maps as anatomical context. However, this NN also significantly removed the noise in the images, which could impact some texture analysis methods or make the images look unrealistic. An auxiliary Noise2Noise model estimates that smoothing effect, enabling texture extraction and transfer to the PR-corrected prediction through Model-consistent Texture Re-Injection (MTRI). In simulated patients, PRC-TP preserved contrast recovery close to ground truth (GT) (98.96-99.04%) while restoring noise and CNR closer to the reference. The function-based MTRI formulation achieved near unity global texture amplitude agreement with GT (0.997 /- 0.011), reducing the input texture amplitude bias (0.951 /- 0.011). Radiomics analysis showed improved agreement with GT across texture-sensitive feature families. A clinical 82Rb evaluation showed trends consistent with simulations, including comparable contrast-ratio increase (10.18% vs. 10.99%) and restoration of texture suppressed by PRC. These results support PRC-TP as a practical framework for resolution recovery with acquisition-consistent texture preservation in PET imaging.
Submitted to IEEE TRPMS.
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