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A Generation Method of Segment-level Fingerprint-based Transformer for Video Partial Copy Detection
Sooyeon Kang, Minsoo Jeong, Jongho Nang
http://doi.org/10.5626/JOK.2023.50.3.257
With the recent generalization of video-capturing devices and the development of various multimedia platforms, video content usage is increasing every year. However, as a side effect of this, copyright infringement crimes regarding video content are also increasing. In this paper, we propose a segment fingerprint generation method for robust video copy detection systems in various transforms to address these problems. We propose a method for generating a frame fingerprint with a hybrid vision transformer, weighting the generated frame fingerprint with a transformer encoder, and fusing it into Maxpooling to aggregate a segment fingerprint. We used the VCDB dataset and measured the F1 score, which was 0.772.
A Fusion of CNN-based Frame Vector for Segment-level Video Partial Copy Detection
http://doi.org/10.5626/JOK.2021.48.1.43
Recently, the demand for media has grown rapidly, led by multimedia content platforms such as YouTube and Instagram. As a result, problems such as copyright protection and the spread of illegal content have arisen. To solve these problems, studies have been proposed to extract unique identifiers based on the content. However, existing studies were designed for simulated transformation and failed to detect whether the copied videos were actually shared. In this paper, we proposed a deep learning-based segment fingerprint that fused frame information for partial copy detection that was robust for various variations in the actually shared video. We used TIRI for data-level fusion and Pooling for feature-level fusion. We also designed a detection system with a segment fingerprint that was trained with Triplet loss. We evaluated the performance with VCDB, a dataset collected based on YouTube, and obtained 66% performance by fusing frame features sampled for 5 seconds with Max pooling for detecting video partial-copy problems.
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