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Advancing Techniques for Deepfake Detection and Evaluation: Challenges and Innovations

Advancing Techniques for Deepfake Detection and Evaluation: ...

引用

Computational Intelligence, Communication Technology and Networking (CICTN), international conference on

作者： Pradum Kumar Singh Sourav Sharma Ritu Sachdeva School of Computer Applications Manav Rachna International Institute of Research & Studies Faridabad India

ISBN: (数字)9798331530389

ISBN: (纸本)9798331530396

The proliferation of deepfakes-also termed artificial intelligence-generated synthetic media poses unprecedented challenges to the digital authenticity of media, for media integrity, and to societies’ trust in its credibility. Despite such significant technological advancements, current methodologies for detecting deepfakes remain somewhat fragmented, reactive, and often unable to keep pace with rapidly evolving technologies in generative AI. This article considers a comprehensive multi-modal approach toward deepfake detection by integrating an advanced machine learning algorithm, novel statistical correlation techniques, and forensic image analysis that would fill in the critical gaps in the existing frameworks. By using an extensive dataset of 10,000 synthetic and authentic media samples from diverse domains, we developed a hybrid neural network architecture that attained 94.3% accuracy on identifying AI-generated content. Our methodology utilized ensemble learning by combining spatial-temporal inconsistency detection, biological signal analysis, and advanced feature extraction algorithms. Key findings demonstrate significant vulnerabilities in current deepfake generation models, with our proposed technique successfully identifying subtle artifacts and inconsistencies across multiple generative AI platforms. This research conclusively demonstrates that proactive, adaptive detection strategies are critical to mitigating the potential risks associated with synthetic media, providing a robust framework for future technological interventions in digital forensics and media authentication.

关键词： Deepfakes Machine learning algorithms Generative AI Neural networks Digital forensics signal processing algorithms Authentication Media Feature extraction Information integrity

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COVID-19 and Pneumonia Detection using X-ray image-Based Deep Learning Methods 2

COVID-19 and Pneumonia Detection using X-ray Image-Based Dee...

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2nd international conference on advanced algorithms and signal image processing, aasip 2022

作者： Li, Yuxiao McGill University Montreal Canada

ISBN: (纸本)9781510660571

Globally, pneumonia is the leading cause of death for young people and children. An X-ray of the chest is usually used to diagnose pneumonia by a trained specialist. However, the process is tedious and can result in disagreements among radiologists. It is possible to improve diagnostic accuracy through the use of computer-aided diagnostic systems. In this work, the ResNet model was selected to work as the covid-19 and pneumonia detector based on X-ray image. Several experiments are conducted on to achieve an optimal results. © 2022 SPIE.

关键词： Deep learning

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The application of low rank matrix decomposition method in image restoration 2

The application of low rank matrix decomposition method in i...

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2nd international conference on advanced algorithms and signal image processing, aasip 2022

作者： Peng, Yue Northwest Minzu University China

ISBN: (纸本)9781510660571

image restoration is a hot issue in the field of image processing. Traditional algorithms approach the original function by a regular function or remove redundant noise by similarity. This process is complicated and cumbersome. In this paper, the low rank approximate matrix of the image matrix is equivalent to the product of two smaller matrices. At the same time, the first-order and second-order statistical information of the image matrix is effectively maintained by using the matrix Frobenius norm and matrix kernel normal. Secondly, the alternating direction multiplier method is utilized to solve the model. Finally, experimental results test the effectiveness of the proposed algorithm. © 2022 SPIE.

关键词： image reconstruction

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Fw-U-Net: a New Satellite image Segmentation of Forest and Water based on U-Net structure 2

Fw-U-Net: a New Satellite Image Segmentation of Forest and W...

引用

2nd international conference on advanced algorithms and signal image processing, aasip 2022

作者： Wang, Wentao Huang, Xuanyao Liu, Mingjie Chongqing University of Posts and Telecommunications No. 2 Chongwen Road CA Chong Qing China

ISBN: (纸本)9781510660571

As global environmental problems are becoming more and more serious, satellite image segmentation and automatic monitoring of forests and water bodies are becoming more and more important in order to prevent natural disasters and monitor natural conditions, including monitoring deforestation and areas affected by floods. In this paper, the traditional U-Net model is improved and a FW-U-Net model suitable for forest water segmentation is proposed. After verification, the segmentation verification scores of the forest coverage area and water area performance test set are 84.37%(83.65%) and 87.92%(85.42%) respectively, which has high accuracy and reference value for forest and water satellite image segmentation. © 2022 SPIE.

关键词： Semantics

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The Effect of Data Augmentation and Depth of networks on CNN-based Breast X-ray image Diagnosis of Pneumonia 2

The Effect of Data Augmentation and Depth of networks on CNN...

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2nd international conference on advanced algorithms and signal image processing, aasip 2022

作者： Wang, Yiming Peking University No.5 Yiheyuan Road Haidian District Beijing China

ISBN: (纸本)9781510660571

COVID-19 has become a worldwide disease that draws researchersҠattention, and people have tried deep learning based methods to boost the diagnosis. It is crucial to design an algorithm for automatic Pneumonia Diagnosis. In this paper, we trained and applied a CNN-based model that is competent to not only diagnose COVID-19 based on breast x-ray images but also do classification on 4 classes, namely normal, COVID-19, viral pneumonia, and bacterial pneumonia. Further, we dive into the effect of data augmentation and depth of networks, and weӶe gained surprising results. © 2022 SPIE.

关键词： COVID-19

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Research on Fundus image Augmentation Algorithm 2

Research on Fundus Image Augmentation Algorithm

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2nd international conference on advanced algorithms and signal image processing, aasip 2022

作者： Yu, Hui Cai, Lanlan Yan, Xue Network Information Center Wuhan University of Technology Wuhan430070 China School of Mechanical and Electronic Engineering Wuhan University of Technology Wuhan430070 China School of Computer and Artificial Intelligence Wuhan University of Technology Wuhan430070 China

ISBN: (纸本)9781510660571

Diabetic retinopathy is a serious ocular complication caused by diabetes, has now become the main reason for the workforce blinding, using digital fundus image for diabetic retinopathy screening regularly is the key to preventing blindness. However, in the process of image acquisition, generation and transmission, it is susceptible to low light conditions and Gaussian noise, and it is difficult to detect small lesions and blood vessels in fundus images, which greatly reduces the accuracy of computer-aided diagnosis. Therefore, this paper proposes an image enhancement algorithm for digital fundus images, using adaptive clipping based on improved Canny edge detection to perform square clipping around fundus region. The improved CLAHE technique was used for low light enhancement to highlight the details of the lesions in the image. Aiming at the additive white Gaussian noise caused by medical image digitalize process, the fundus image denoising algorithm based on self-supervised EM-GMM is used to suppress the influence of noise by imposing sparsity constraint on covariance eigenvalues. The experiment shows that our methods achieve good performance in DDR dataset. © 2022 SPIE.

关键词： Computer aided diagnosis

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ECT image reconstruction based on improved multi-scale residual network 2

ECT image reconstruction based on improved multi-scale resid...

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2nd international conference on advanced algorithms and signal image processing, aasip 2022

作者： Ma, Min Wu, Huan Civil Aviation University of China Tianjin300300 China

ISBN: (纸本)9781510660571

In order to solve the soft field effect in capacitance tomography lead to the problem of low quality of image reconstruction, this paper proposes an image reconstruction algorithm based on improved multi-scale residual network(IMRN), by introducing a multi-scale convolution structure layer information, abundant feature extracting multi-scale empty convolution structure, and then build a change with different expansion rate convolution receptive field, The global feature information is obtained and the number of network parameters is effectively reduced. The channel attention mechanism is used to weight the extracted features adaptively and filter the redundant information. Finally, the shallow features and the extracted features of each structure are fused to compensate the lost feature information. Simulation results show that compared with LBP algorithm, Landweber iterative algorithm and 1DCNN algorithm, the improved algorithm effectively improves the quality of image reconstruction. © 2022 SPIE.

关键词： Convolution

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Self-attention mechanism fusion method for bi-modal images 2

Self-attention mechanism fusion method for bi-modal images

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2nd international conference on advanced algorithms and signal image processing, aasip 2022

作者： Junqing, Li Jiongyao, Ye School of Information Science and Engineering East China University of Science and Technology Shanghai200237 China

ISBN: (纸本)9781510660571

Multimodal information fusion can extract the important feature information of each modality and realize the information complementation between the modalities, which is a research direction of considerable interest at present. Extracting texture information from visible images and extracting typical features from infrared images are the main roles of dual-modal image fusion. To address the problems of information loss and low clarity of current deep learning-based fusion methods, this paper proposes a method to fuse bimodal images by embedding a network with attention mechanism. Due to the problems of low resolution and noise in infrared images, this method specifically uses encoder structures with different depths for infrared and visible images to extract shallow and deep features of each image, and then passes them through the attention-based fusion network to obtain the fused feature maps. A one-dimensional convolutional model based on a local cross-channel interaction mechanism without dimensionality reduction is used to construct the channel attention module in the fusion network, thus reducing the network complexity and improving the overall performance. The final fused image in this method relies on the decoder structure to generate. Experiments show that the method in this paper can fuse bimodal information better, and has some visual improvement under subjective evaluation compared with the comparison algorithm, and has 7.82%, 9.46%, 19.14% and 13.85% improvement in various objective evaluation indexes compared with DeepFuse, DenseFuse, FusionGAN and GANMcC respectively. © 2022 SPIE.

关键词： image fusion

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Self2Align: A Self-Supervised Denoising Framework for Single-Scene images 2

Self2Align: A Self-Supervised Denoising Framework for Single...

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2nd international conference on advanced algorithms and signal image processing, aasip 2022

作者： Wang, Jian Heilongjiang University of Science and Technology Harbin China

ISBN: (纸本)9781510660571

In recent years, driven by the development of deep neural networks, image denoising technology has made great progress. One of the most representative technologies is the deep neural network denoiser based on supervised learning. The denoiser uses noise-clean image pairs as the input and output of a deep neural network, and trains the deep neural network to achieve the denoising goal. However, collecting many high-quality noise-clean image pairs is extremely challenging, which is mainly because (1) it is difficult to collect true and clean images;and (2) changes in motion and lighting make it impossible to align collected image pairs, which limits the widespread application of supervised learning denoising techniques. To solve the above problems, this paper proposes a simple and effective method Self2Align to train a deep neural network denoiser. First, we proposed an efficient deep network model for inter-image alignment. For the collection of original images, we only use noisy images and collected multiple images for different scenes. The trained alignment network was then used to align the original image pairs automatically. Second, the aligned image pairs generated in the first stage were used as training image pairs for the training of the denoising network. In addition, we introduced a new training strategy so that the network can obtain better performance. The proposed Self2Align architecture eliminates the reliance on noise-clean image pairs and reduces the acquisition difficulty of training image pairs in terms of self-supervised training of the network. We explained the feasibility of our proposed method through theoretical analysis and obtained competitive results through experimental verification. © 2022 SPIE.

关键词： image denoising

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Second international conference on advanced algorithms and signal image processing, aasip 2022

Second International Conference on Advanced Algorithms and S...

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2nd international conference on advanced algorithms and signal image processing, aasip 2022

ISBN: (纸本)9781510660571

The proceedings contain 74 papers. The topics discussed include: design of muscle state sensing system based on flexible micro airbag structure;classification on normal chest x-ray and pneumonia x-ray;research and design of image super-resolution algorithm based on attention mechanism;automatic segmentation of thyroid nodule from ultrasound images using spatial-channel attentive u-net;research on line spectrum extraction based on improved discrete particle swarm;research on fundus image augmentation algorithm;human joint pose estimation based on the Kalman filter;self-attention mechanism fusion method for bi-modal images;and the application of low rank matrix decomposition method in image restoration.

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