Automatic Generation of H.264 Parameter Sets to Recover Video File Fragments

Altinisik, Enes; Sencar, Hüsrev Taha

doi:10.1109/tifs.2021.3118876

Cited by 3 publications

(7 citation statements)

References 21 publications

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“…Among the existing file encoding formats, H.264 is by far the most widely used codec today [27], [28]. In terms of file container formats, despite a great diversity of options, mobile phones predominantly use file formats that extend over the MPEG-4 standard [33].…”

Section: Video File Generationmentioning

confidence: 99%

“…To make such video file fragments playable, [28] introduced a method for estimating encoding parameters through a search over parameter space by incorporating decoding error messages. Although the space of parameter values defined by the H.264 standard is extremely large, many parameters vary slightly across camera models and for several parameters choosing the highest or lowest categorical setting will not curtail decoding.…”

Section: Tackling Partial Video Filesmentioning

confidence: 99%

“…Given this capability, we also investigate how reliably the source camera-model of a video can be identified using a subset of coding parameters. With this objective, we apply the method in [28] to coded frame data extracted from videos to estimate their coding parameters with one modification. In its operation, this method does not identify frame height as a core parameter because an incorrect setting causes the decoded frame to be either truncated or elongated through content repetition.…”

Section: Tackling Partial Video Filesmentioning

confidence: 99%

“…This test setting concerns the extreme case where only a minimal subset of encoding parameters are available for camera-model identification. For this, the automatic header generation method of [28] is used to estimate the values of the 10 core parameters along with the picture height from original (unedited) videos captured by a camera. We must note here that changing the values of encoding parameters does not necessarily cause a decoding failure.…”

Section: E Partial Video Filesmentioning

confidence: 99%

“…This corresponds to an investigative setting encountered frequently in the process of recovering files from storage devices during which file metadata is largely unavailable. In such cases, some encoding parameters can be determined through analysis of coded frame data as demonstrated in [28]. Hence, parameters estimated from incomplete video files can also be used to infer the camera model.…”

Section: Introductionmentioning

confidence: 99%

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Camera-Model Identification Using Encoding and Container Characteristics of Video Files

Altinisik¹,

Sencar²,

Tabaa³

2022

Preprint

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We introduce a new method for camera-model identification. Our approach combines two independent aspects of video file generation corresponding to video coding and media data encapsulation. To this end, a joint representation of the overall file metadata is developed and used in conjunction with a two-level hierarchical classification method. At the first level, our method groups videos into metaclasses considering several abstractions that represent high-level structural properties of file metadata. This is followed by a more nuanced classification of classes that comprise each metaclass. The method is evaluated on more than 20K videos obtained by combining four public video datasets. Tests show that a balanced accuracy of 91% is achieved in correctly identifying the class of a video among 119 video classes. This corresponds to an improvement of 6.5% over the conventional approach based on video file encapsulation characteristics. Furthermore, we investigate a setting relevant to forensic file recovery operations where file metadata cannot be located or are missing but video data is partially available. By estimating a partial list of encoding parameters from coded video data, we demonstrate that an identification accuracy of 57% can be achieved in camera-model identification in the absence of any other file metadata.

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Section: Video File Generationmentioning

confidence: 99%

Section: Tackling Partial Video Filesmentioning

confidence: 99%

Section: Tackling Partial Video Filesmentioning

confidence: 99%

Section: E Partial Video Filesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Camera-Model Identification Using Encoding and Container Characteristics of Video Files

Altinisik¹,

Sencar²,

Tabaa³

2022

Preprint

Self Cite

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ByteRCNN: Enhancing File Fragment Type Identification With Recurrent and Convolutional Neural Networks

Skračić,

Petrović,

Pale

2023

IEEE Access

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File fragment type identification is an important step in file carving and data recovery. Machine learning techniques, especially neural networks, have been utilized for this problem, some with very promising results. This paper presents a novel neural network architecture for identifying file fragment types using a combination of byte embeddings as well as recurrent and convolutional elements. The corresponding classification model, ByteRCNN, has been trained on the publicly available file fragment FiFTy dataset and evaluated in closed-set and open-set recognition settings using FiFTy and other available file fragment datasets. Evaluation results have demonstrated that ByteRCNN can compete with state-of-the-art models described in literature in terms of classification accuracy, with 71.1% average accuracy on 512-byte fragments and 83.9% average accuracy on 4,096-byte fragments from the FiFTy dataset. When evaluated on other publicly available datasets in closed-set and open-set recognition settings, ByteRCNN similarly or slightly better than the FiFTy classification model. Obtained results overall suggest that ByteRCNN is a competitive file fragment classification model, but they also reveal that there is still plenty of space for further improving file type identification methods using more complex datasets or in open-set recognition settings. ByteRCNN is publicly available at [GitHub, after publication].

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