Video Snippet Inconsistency Detection for Edited Content
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Solution Overview
Problem
Current video detection methods face challenges in accurately determining whether an object in a video is edited, particularly due to the neglect of short-term inconsistencies and inadequate modeling of local motion, leading to low accuracy in identifying edited content.
Innovation Solution
The proposed method extracts video snippets from a video, using both intra-snippet and inter-snippet representation vectors to model local motion and establish a dynamic inconsistency model, which is then integrated into a convolutional neural network to enhance the detection of edited objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image-based detection method is used to perform editing detection by mining discriminative features at frame level, then the detection can be performed at frame level, but the accuracy deteriorates because forgery traces at frame level can be hardly caught
Solution Approach 1:
The video is segmented into multiple video snippets, each containing multiple frames. This segmentation allows the system to analyze temporal inconsistencies within and across snippets, capturing short-term motion patterns that are invisible at the frame level alone. The segmentation transforms the detection problem from static frame analysis to dynamic temporal pattern recognition.
Solution Approach 2:
The patent introduces a temporal dimension by creating intra-snippet representation vectors that capture motion patterns across frames within a snippet. This adds a time-based dimension to the detection, moving beyond spatial feature analysis at single frames to temporal inconsistency analysis across multiple frames, thereby revealing forgery traces that are imperceptible at the frame level.
2Reliability
If video-based face editing detection is regarded as a video-level representation learning problem and only long-term inconsistency is modeled, then the detection can be performed at video level, but the accuracy deteriorates because short-term inconsistency is completely ignored
Solution Approach 1:
The video is divided into multiple video snippets, each processed to generate representation vectors. This segmentation enables the system to capture both short-term inconsistencies within individual snippets and long-term inconsistencies across multiple snippets, combining local temporal patterns with global temporal patterns for comprehensive detection.
Solution Approach 2:
The patent merges intra-snippet representation vectors (capturing short-term motion inconsistencies) with inter-snippet representation vectors (capturing long-term temporal inconsistencies) to form a comprehensive video representation. This combination integrates both short-term and long-term temporal information, resolving the limitation of ignoring short-term inconsistency in traditional video-level detection.
3Device complexity
If traditional video detection methods are used that neglect short-term inconsistencies and inadequate modeling of local motion, then the detection process is simpler, but the accuracy deteriorates in identifying edited content
Solution Approach 1:
The patent introduces dynamic inconsistency modeling by computing representation vectors that capture temporal motion patterns within and across video snippets. This dynamic approach contrasts with static frame-level or video-level analysis, enabling the system to adaptively detect inconsistencies in motion patterns that characterize edited content while maintaining a structured processing framework.
Data Source
AI summary
A video detection method includes extracting N video snippets from a video, each video snippet of the N video snippets comprising M frames, and determining a probability that an initial object in the video is an edited object. The probability is determined based on intra-snippet representation vectors and inter-snippet representation vectors, each intra-snippet representation vector corresponding to a respective video snippet of the N video snippets and representing inconsistent information between frames in the respective video snippet of the N video snippets, and each inter-snippet representation vector corresponding to a respective video snippet of the N video snippets and representing inconsistent information between the respective video snippet and one or more adjacent video snippets of the N video snippets.