Deepfake Detection System Utilizing ResNeXt for Feature Extraction and LSTM for Temporal Analysis

Abstract

The present invention relates to a deepfake detection system that combines ResNeXt-based spatial feature extraction and Long Short-Term Memory (LSTM)-based temporal analysis to identify deepfake videos. The system processes input video data by first extracting detailed spatial features from individual frames using the ResNeXt architecture, followed by temporal analysis using LSTM networks to capture frame-to-frame inconsistencies. This hybrid approach allows for robust and accurate detection of deepfakes by leveraging both spatial and temporal cues. The system is designed for scalability and can be deployed in real-time or near real-time applications across various domains.

Specification

Description:FIELD OF THE INVENTION: The present invention relates to the field of artificial intelligence and machine learning, specifically to a deep learning-based system for detecting deepfakes. The invention utilizes ResNeXt for enhanced feature extraction and Long Short-Term Memory (LSTM) networks for temporal analysis of video data to improve the accuracy of deepfake detection. 3. BACKGROUND OF THE INVENTION: The rise of deepfake technology, which involves manipulating images or videos using artificial intelligence to create realistic, fake content, has introduced significant concerns related to misinformation, privacy, and security. Deepfakes are created using generative adversarial networks (GANs) and other sophisticated techniques that can alter faces, voices, or entire scenes in ways that are difficult to detect with the human eye. Traditional methods of detecting deepfakes rely on shallow classifiers or basic convolutional neural networks (CNNs) that primarily analyze spatial features within images or video frames. However, these methods fail to capture the subtle temporal inconsistencies that often occur between frames in a video deepfake. Additionally, existing detection systems face challenges in scalability and robustness against evolving deepfake techniques. This invention addresses these limitations by introducing a hybrid deep learning system that combines ResNeXt, a CNN-based architecture, for spatial feature extraction and LSTM networks for analyzing temporal dynamics within videos. This combination allows for more accurate detection of deepfakes by capturing both spatial and temporal features of the manipulated content. ________________________________________ 4. OBJECTIVES OF THE INVENTION: The primary objectives of the present invention are: 1. To provide an accurate and robust system for detecting deepfake videos. 2. To enhance deepfake detection by combining spatial and temporal feature extraction. 3. To use the ResNeXt architecture for extracting high-level spatial features from video frames. 4. To utilize LSTM networks for capturing temporal inconsistencies across video frames. 5. To provide a scalable and efficient deep learning system that can be deployed in real-time or near real-time environments. 6. To detect deepfakes across various domains, including security, media, and social networks. ________________________________________ 5. SUMMARY OF THE INVENTION: The present invention provides a deepfake detection system that utilizes ResNeXt, an advanced CNN model, for extracting detailed spatial features from video frames, and LSTM networks for analyzing temporal inconsistencies between video frames. By combining these two architectures, the system can detect deepfakes more effectively by leveraging both spatial and temporal cues. The invention involves processing input video data through the ResNeXt architecture to extract spatial features, followed by feeding these features into the LSTM network to analyze the temporal sequences across multiple frames. The LSTM captures temporal dependencies and identifies frame-to-frame inconsistencies that are typical in deepfakes. The final output is a classification result indicating whether the video is real or a deepfake. The system is designed to be scalable, robust against evolving deepfake techniques, and applicable to various domains such as media, law enforcement, and content moderation on social platforms. ________________________________________ 6. DETAILED DESCRIPTION OF THE INVENTION: 1. Overview of the Deepfake Detection System: The deepfake detection system consists of the following components: • ResNeXt-based Feature Extraction Module: This module is responsible for extracting spatial features from each frame of the input video. ResNeXt is an advanced CNN architecture that uses group convolution to improve feature extraction, allowing the system to capture fine-grained details that may indicate deepfake manipulation. • LSTM-based Temporal Analysis Module: This module analyzes the temporal sequences across frames. LSTMs are well-suited for capturing long-term dependencies in sequences, making them ideal for identifying frame-to-frame inconsistencies that are characteristic of deepfake videos. • Classification Module: The extracted features from ResNeXt and the temporal dynamics analyzed by LSTM are fed into a classifier that determines whether the video is a deepfake or not. 2. Working of the System: • Step 1: Video Input Preprocessing The system receives an input video, which is preprocessed by extracting individual frames. These frames are resized and normalized to match the input size requirements of the ResNeXt model. • Step 2: ResNeXt-Based Spatial Feature Extraction Each frame of the video is passed through the ResNeXt architecture, which extracts spatial features from the images. ResNeXt uses a modular design with group convolutions, enabling efficient feature extraction while maintaining high accuracy. The extracted features represent various spatial characteristics, such as edges, textures, and patterns, which are critical for detecting manipulations. • Step 3: Temporal Analysis Using LSTM The spatial features from the ResNeXt model are then fed into the LSTM network. The LSTM processes the sequence of features across multiple frames, capturing temporal relationships and identifying inconsistencies that may indicate a deepfake. Since deepfake videos often exhibit temporal artifacts, such as unnatural transitions or misalignment between frames, the LSTM can effectively model these temporal anomalies. • Step 4: Deepfake Classification The features processed by the ResNeXt and LSTM modules are combined and passed to the final classification layer. The classifier uses these features to predict whether the video is real or a deepfake. The output is a probability score indicating the likelihood of the video being a deepfake, along with a binary classification result (real or deepfake). • Step 5: Post-processing and Output Once classified, the system provides the result to the user, along with a confidence score. If required, the system can also provide insights into the specific temporal or spatial features that led to the detection, aiding in interpretability. 3. Advantages of the Invention: • High Detection Accuracy: By leveraging ResNeXt for spatial feature extraction and LSTM for temporal analysis, the system can detect deepfakes with higher accuracy compared to conventional methods that rely solely on CNNs. • Robustness Against Evolving Deepfake Techniques: The hybrid architecture ensures that both spatial artifacts (e.g., facial distortions) and temporal anomalies (e.g., unnatural frame transitions) are detected, making the system robust against evolving deepfake generation techniques. • Scalability: The system is scalable and can be deployed on various platforms, including cloud-based systems, edge devices, or local servers, enabling real-time or near real-time deepfake detection. • Versatility: The system can be applied to a variety of domains such as media verification, online content moderation, video authentication for law enforcement, and more. , Claims:1.I/We Claim a deepfake detection system, comprising:  a ResNeXt-based feature extraction module configured to extract spatial features from individual frames of a video input;  a Long Short-Term Memory (LSTM)-based temporal analysis module configured to analyze the temporal sequences of the extracted features across multiple frames; and  a classification module configured to classify the video as either a real video or a deepfake based on the extracted spatial and temporal features. 2. I/We Claim the system as claimed in claim 1, wherein the ResNeXt-based feature extraction module utilizes group convolutions for efficient spatial feature extraction. 3. I/We Claim the system as claimed in claim 1, wherein the LSTM-based temporal analysis module is configured to capture long-term dependencies between frames to identify temporal inconsistencies indicative of deepfakes. 4. The system as claimed in claim 1, wherein the classification module outputs a probability score indicating the likelihood of the video being a deepfake, along with a binary classification result. 5. I/We Claim the system as claimed in claim 1, further comprising a preprocessing module for extracting and normalizing video frames prior to feature extraction by the ResNeXt module.

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