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Optimization of DeepFake Video Detection Using image Preprocessing 5

Optimization of DeepFake Video Detection Using Image Preproc...

引用

5th international conference on Advances in Computational Tools for Engineering applications, ACTEA 2023

作者： Berjawi, Ali Samrouth, Khouloud Deforges, Olivier Arab Open University Cybersecurity and Forensics Department Beirut Lebanon IETR INSA Rennes France

ISBN: (纸本)9798350309959

Deep learning has been evolving recently which allowed it to handle complex problems like big data, computer vision, and human-level control. One of the deep learning-powered applications recently emerged is called 'deepfake'. Deepfake algorithms have recently been a controversial development in Artificial Intelligence, because they use deep learning to generate fake yet realistic content based on an input dataset. As a result, many are concerned with the potential risks in terms of cyber-security as it causes threats to privacy, democracy, and national security. Multiple techniques were proposed to detect deepfake videos, however most cannot cope with the variety of the deepfake generation techniques. Therefore, in this study, we optimize one of the best existing deepfake detection methods based on Xception model. In particular, our proposed optimization scheme consists of a pre-processing phase performing advanced image enhancement on the videos in hand for highlighting the face features for better feature extraction as well fake content detection, which is preceded by a close-up dataset cleansing. Our experiments show that the proposed pre-processing optimization scheme had improvemes the performance of the Xception Binary Classifier- Inference model from 94% to 96%. © 2023 ieee.

关键词： image enhancement

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POINT CLOUD COMPLETION BY MINIMIZING PREDICTION ERRORS IN BOTH 2D AND 3D SPACES 29

POINT CLOUD COMPLETION BY MINIMIZING PREDICTION ERRORS IN BO...

引用

ieee international conference on image processing (ICIP)

作者： Mok, Yeongheon Kim, Mingi Kim, Heegwang Paik, Joonki Chung Ang Univ Grad Sch Artificial Intelligence Seoul 06974 South Korea Chung Ang Univ Dept Image Seoul 06974 South Korea

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

Early works for 3D point cloud completion primarily focused on revising deep learning networks, and showed an acceptable performance. However, the performance those methods is limited because of lack of geometric studies. To solve that problem, we used multiple loss functions that allow the deep learning networks to fully understand the geometric perspective of the object in both two-dimensional (2D) and three-dimensional (3D) spaces. As a result, the angle between the prediction and ground truth vectors is reduced for accurate local details in the 3D space, and at the same time, the difference in distance on a specific plane is reduced for 2D local details. For that reason, the proposed point cloud completion method can lead to clarify semantic information and structure of 3D objects. In addition, the proposed mathematical approach can give better understanding of object, which can improve the performance of 3D point cloud completion.

关键词： Point cloud completion Geometry-aware deep learning Projection loss

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Efficient Approximate Adders for image processing applications

Efficient Approximate Adders for Image Processing Applicatio...

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2024 international conference on Advances in Computing Research on Science Engineering and Technology, ACROSET 2024

作者： Padma, C. Potladurty, Suresh Babu Nalini, C. Suguna, T. Pallavi, C.H. Sri Venkateswara College of Engineering Department of Ece Tirupati India Mohan Babu University Department of Ece Tirupati India Panimalar Engineering College Department of Ece Chennai India

ISBN: (纸本)9798350388800

This effort sacrificed accuracy in order to design fast, energy-efficient adders. In order to minimize propagation delay and reduce power consumption, the proposed design truncates half of the adder area. Additionally, internal stage input-output pipelining to the parallel prefix adder can further optimize propagation delay to half of its original value. By significantly raising the approximation adder error, the MSP minimizes the parameters while utilizing the exact calculation and LUTs as resources. In comparison to the current LEADx and APEx, our proposed adder is now efficient in terms of parameters. Xilinx Vivado is used to synthesize the design, simulation, and efficacy of the suggested method. As a case study, the suggested approximation adders are applied in a video encoding application. For video encoding applications, LEADx provided superior quality when compared to other types of approximation adders. Thus, our suggested approximate adders can be useful in efficient FPGA designs of error-tolerant applications. © 2024 ieee.

关键词： Encoding (symbols)

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Dynamic Fire Detection and Alerting Using image processing for Drone applications

Dynamic Fire Detection and Alerting Using Image Processing f...

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2024 international conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Sawadogo, Abdoul Moumouni Mohan, C. Nelly, Dina Din Danielle Lata, Suman Sharda University Department of Electrical Electronics and Communication Engineering Greater Noida India

ISBN: (纸本)9798350378092

In this paper, a technique has been proposed for a dynamic fire detection system based on Python opencv image processing. On the one hand, consider the system consists of a quadcopter/drone equipped with a mobile phone having the IP webcam app for live video streaming, and the drone is controlled autonomously using a PC, dedicated software like mission planner. On the other hand, another PC with a Python algorithm is used for the processing of the video image to detect fire. The dynamic approach consists in capturing the live stream webcam and detecting any fire presence in the video frame with the HAAR cascade classifier dataset and all this in real-time. After takeoff of the drone, the area mapping is done through waypoints on the Mission planner. A second PC (fire control) is used to start the live stream, detect fire and make decisions. The webcam IP address is fed into the algorithm to extract the video frame. The frame is in RGB format and with the help of the pre-trained model along with the threshold parameters used;the algorithm can detect fire in the live streaming coming from the drone's camera. Once the fire is detected, the frame becomes stable for some time so the operator sitting in the control room can keep track of the event happening. Functionality is added to the code to track some specific pixels (red, blue, and white) in the fire-detected region and display approximate temperature values based on preset thresholds of these colors. Since it is very difficult to measure the temperature on the image without using a thermal camera and this logic is a novel one. Also, logic is used to capture a fire incident into a *** image and send it with an email as an attachment to the concerned fire department after a 6s delay. The attachment image is a very important and unique feature as it reveals to the fire department the type of incidents that occurred and even the area if the camera is good enough. © 2024 ieee.

关键词： Video streaming

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MULTI-FRAME VIDEO PREDICTION WITH LEARNABLE MOTION ENCODINGS 29

MULTI-FRAME VIDEO PREDICTION WITH LEARNABLE MOTION ENCODINGS

引用

ieee international conference on image processing (ICIP)

作者： Jasti, Rakesh Jampani, Varun Sun, Deqing Yang, Ming-Hsuan UC Merced Merced CA 95343 USA Google Res Sunnyvale CA USA

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

Predicting multiple future frames from a given video is a challenging problem due to several factors such as changing camera, dynamically moving objects, occlusions, etc. While recent deep learning methods have made significant progress on the video prediction problem, most methods predict the immediate or a fixed number of future frames. To obtain longer-term frame predictions, existing techniques usually process the predicted frames iteratively, resulting in blurry or inconsistent predictions. In this work, we present a new approach that can predict an arbitrary number of future video frames with a single forward pass through the network. Instead of directly predicting a fixed number of future optical flows or frames, we learn temporal motion encodings, i.e., temporal motion basis vectors and a network to predict the coefficients. The learned motion basis can be easily extended to arbitrary length at inference time, enabling us to predict an arbitrary number of future frames. Experiments on benchmark datasets show that our approach performs favorably against several competitive techniques even for the next frame prediction setting. When evaluated under 5-frame or 10-frame prediction settings, the proposed method achieves higher performance gains over the state-of-the-art techniques that iteratively process the predictions.

关键词： Deep learning Knowledge engineering image coding Neural networks Benchmark testing Performance gain Cameras

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DCAN: A DUAL CASCADE ATTENTION NETWORK FOR FUSING PET AND MRI imageS 29

DCAN: A DUAL CASCADE ATTENTION NETWORK FOR FUSING PET AND MR...

引用

ieee international conference on image processing (ICIP)

作者： Du, Yifan Li, Bicao Liu, Zhoufeng Li, Chunlei Shao, Zhuhong Wang, Zongmin Zhongyuan Univ Technol Sch Elect & Informat Engn Zhengzhou 450007 Peoples R China Zhengzhou Univ Sch Informat Engn Zhengzhou 450001 Peoples R China Capital Normal Univ Coll Informat Engn Beijing 100048 Peoples R China

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

Traditional fusion approaches and most deep learning-based methods usually generate the intermediate decision map, resulting in detail loss of source images or fusion results. In this work, to enhance the detailed features and structured information from source images, we propose a dual cascade attention network (DCAN) to obtain a more informative fusion image for PET and MRI images. In our approach, channel attention is employed to improve the ability of features representation and spatial attention can highlight informative regions in the proposed fusion network. Additionally, channel and spatial attention are sequential arrangement in channel-first. Moreover, to achieve good performance in the procedure of feature extraction and image reconstruction, two-stage training strategy is adopted to train our fusion model. Experimental results demonstrate that the proposed approach achieves remarkable performance for PET and MRI images fusion.

关键词： image fusion End-to-end network Dual cascade attention network Residual structure PET and MRI images

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Sleep Apnea Detection System Using Machine Learning on Resource-Constrained Devices 17

Sleep Apnea Detection System Using Machine Learning on Resou...

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17th Annual ieee international Systems conference (SysCon)

作者： Mallick, Sayani Gawali, Shubhangi Onime, Clement Goveas, Neena BITS Pilani EEE Dept Goa Campus Sancoale Goa India BITS Pilani CSIS Dept Goa Campus Sancoale Goa India Abdus Salaam Int Ctr Theoret Phys Trieste Italy

ISBN: (纸本)9781665439947

Sleep Apnea is a condition in which a person has pauses in breathing or very low breathing episodes during sleep. It is a condition that could prove life-threatening if not monitored and treated. A medical diagnosis of Sleep Apnea involves overnight recording of body signals, monitoring by a medical professional, use of hospital based equipment and data analysis for detection of anomalies. During the past decade, the measurement and analysis of human body signals using machine learning techniques on embedded devices have started to transform healthcare applications. The use of cost effective micro-controllers can ensure that health monitoring is available and accessible to all. In this paper, we show that machine learning models deployed on microcontrollers can successfully analyze ECG signals in real-time for Sleep Apnea detection. We have created TinyML models using TensorFlow Lite which we have deployed on cost effective and resource constrained devices like the Raspberry Pi Pico and ESP32. Our setup has given results comparable to more advanced and expensive devices for the detection of Sleep Apnea using ECG signals.

关键词： ECG Embedded Device signal processing TinyML TensorFlow Lite Machine Learning Edge Impulse Sleep Apnea Artificial Neural Networks Edge Computing

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TOWARDS LIGHTWEIGHT applications: ASYMMETRIC ENROLL-VERIFY STRUCTURE FOR SPEAKER VERIFICATION 47

TOWARDS LIGHTWEIGHT APPLICATIONS: ASYMMETRIC ENROLL-VERIFY S...

引用

47th ieee international conference on Acoustics, Speech and signal processing (ICASSP)

作者： Li, Qingjian Yang, Lin Wang, Xuyang Qin, Xiaoyi Wang, Junjie Li, Ming Lenovo Res AI Lab Beijing Peoples R China Duke Kunshan Univ Data Sci Res Ctr Kunshan Peoples R China

ISBN: (纸本)9781665405409

With the development of deep learning, automatic speaker verification has made considerable progress over the past few years. However, to design a lightweight and robust system with limited computational resources is still a challenging problem. Traditionally, a speaker verification system is symmetrical, indicating that the same embedding extraction model is applied for both enrollment and verification in inference. In this paper, we come up with an innovative asymmetric structure, which takes the large-scale ECAPA-TDNN model for enrollment and the small-scale ECAPA-TDNNLite model for verification. As a symmetrical system, our proposed ECAPA-TDNNLite model achieves an EER of 3.07% on the Voxceleb1 original test set with only 11.6M FLOPS. Moreover, the asymmetric structure further reduces the EER to 2.31%, without increasing any computational costs during verification.

关键词： lightweight speaker verification asymmetric enroll-verify structure ECAPA-TDNNLite

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Attention Mechanism Based Technical Phrase Extraction for Patent Text 8

Attention Mechanism Based Technical Phrase Extraction for Pa...

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8th international conference on Intelligent Computing and signal processing, ICSP 2023

作者： Jiang, Xin Zhou, Peng Zhao, Shu Guo, Yufeng Anhui University School of Computer Science and Technology Hefei China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Hefei China

ISBN: (纸本)9798350302455

Technological innovation is becoming one of the critical factors in promoting social development all over the world. The vigorous development of patent applications in recent years provides an opportunity to reveal the inherent laws of innovation, but it also puts forward higher requirements for patent mining technology. An essential step in patent text mining is to build a technical portrait for each patent, that is, to identify the technical phrases involved, which can summarize and represent the patent from a technical perspective. Previous technical phrase extraction methods thoroughly used technical phrases' characteristics and the relationship between technical phrases. Regarding our observations, the relationship between patent texts and technical phrases is also essential. Specifically, critical technical phrases are more relevant to the patent text and can be discovered by the attention mechanism. Motivated by this, we propose an unsupervised technical phrase extraction method based on the attention mechanism named UTESC. Self-attention captures the importance of technical phrases in sentences, and cross-attention captures the relevance between technical phrases and patents. Extensive experiments and algorithm comparisons on patent datasets have proven the effectiveness of our algorithm. © 2023 ieee.

关键词： Patents and inventions

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Hybrid Deep Learning for Multi-Modal image Fusion: Elevating Brain Tumor Detection 2

Hybrid Deep Learning for Multi-Modal Image Fusion: Elevating...

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2nd international conference on Recent Trends in Microelectronics, Automation, Computing, and Communications Systems, ICMACC 2024

作者： Mehta, Shiva Singh, Amanveer Chitkara University Institute of Engineering and Technology Centre for Research Impact & Outcome Punjab Rajpura140401 India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India

ISBN: (纸本)9798350366570

This work presents a new dual deep learning framework that incorporates CNNs and ViTs into the multi-modal medical picture fusion to enhance the diagnostic accuracy of the diagnosed brain tumour. This integration concerns MRI and PET information, applying the ability of CNNs to handle local spatial information and ViTs in collecting long-range relationships. Here, to test the efficacy of the proposed model, a publicly available multi-modal brain tumour database was used, where significant performance improvements were achieved over the conventional techniques. The developed model achieved a sensitivity of 96.8%, while the traditional CNN-based methods attained a sensitivity of only 91.2%. In addition, the research used it to achieve a high accuracy rate of 95.7 percent, a recall rate of 95.1 percent, and an F1 score of 95.4 percent for infallibility in locating tumors with minimal false positives and negatives. It was realized that the model in this study provided improved picture quality, with an SSIM of 0.89 compared to the 0.82 of earlier fusion techniques. The pilot qualitative study confirmed that the work of the fused pictures showed a better combination of PET and MRI information by merging spatial features with metabolic data to help distinguish the tumour's boundaries more clearly and identify small or irregular form tumours. The proposed CNN-ViT model outperforms other methods for detecting tumors with high levels of accuracy and high-quality images that give medicine a vital tool. The methodology has high potential to be applied to other medical imaging jobs as well. © 2024 ieee.

关键词： Medical imaging

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