CNN-Based Video Codec Classifier For Multimedia Forensics

In video forensics, identification of codec type is complicated by a lack of standards compliant compressed bitstreams. Previous work is unable to identify codec types without actually decoding the file successfully. This paper presents a CNN classifier derived from the AlexNet architecture that can...

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Bibliographic Details
Published in2021 IEEE International Conference on Image Processing (ICIP) pp. 3033 - 3037
Main Authors Pessoa, Rodrigo, Kokaram, Anil, Pitie, Francois, Sugrue, Mark
Format Conference Proceeding
LanguageEnglish
Published IEEE 19.09.2021
Subjects
CNN
codec
Conferences
decoding
Fingerprint recognition
Forensics
Image processing
Rendering (computer graphics)
Streaming media
User-generated content
video
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Summary:In video forensics, identification of codec type is complicated by a lack of standards compliant compressed bitstreams. Previous work is unable to identify codec types without actually decoding the file successfully. This paper presents a CNN classifier derived from the AlexNet architecture that can detect codec types without decoding the bitstream. It is based on classification of the raw bitstream data itself without decoding. The algorithm is tested on real data in a video forensics setting as well as user generated content supplied in the YouTube test set. Our results show better than 96.73% accuracy with over 43 combinations of codec/containers and, at least, 88.59% accuracy at 20% data corruption across both test sets.
ISSN:2381-8549
DOI:10.1109/ICIP42928.2021.9506020