Video data recovery from slack space of pre‐allocated video files

The recovery of video evidence from dashboard cameras is often compromised by damage, file system errors, or circular recording mechanisms that overwrite older data. Traditional digital forensic techniques primarily focus on file carving and frame recovery from the unallocated area of storage media. However, this approach is often insufficient for dashcams that use pre-allocation strategies, where fixed-size files are created before recording. As a result, residual data from previous recordings may remain in unused portions of these allocated files but are typically overlooked during forensic investigations. In this study, we identify and define a forensic artifact termed "video file slack," which exists within the allocated space of pre-generated files and contains valid video data from previous recording cycles. To our knowledge, this study is among the first to investigate video recovery from slack regions created by dashcam pre-allocation mechanisms. We propose a method to extract the slack area from common container formats (AVI, MP4, and ASF) and reconstruct fragmented H.264 video streams using frame-based recovery techniques. Experimental results show that the proposed method expands the recoverable area by up to 966.7% compared with traditional analysis limited to the unallocated area. A hit-and-run case study further demonstrates that frames capturing the moment of impact can be recovered from video file slack even when no data are recovered from the unallocated area. These findings indicate that Video File Slack is a valuable source of forensic evidence in dashcam storage systems and that slack-based recovery can improve the completeness of digital forensic investigations.

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Publication Details

Journal
Journal of Forensic Sciences
Published
2026-07-16
DOI
https://doi.org/10.1111/1556-4029.70412
Primary Topic
Digital and Cyber Forensics
Type
article
Field-Weighted Citation Impact
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article

Video data recovery from slack space of pre‐allocated video files

Kyu‐Sun Shim, Junseok Byun, Yong Wook Kim
Journal of Forensic Sciences
Digital and Cyber Forensics
article

Video data recovery from slack space of pre‐allocated video files

Kyu‐Sun Shim, Junseok Byun, Yong Wook Kim
article en

Abstract

The recovery of video evidence from dashboard cameras is often compromised by damage, file system errors, or circular recording mechanisms that overwrite older data. Traditional digital forensic techniques primarily focus on file carving and frame recovery from the unallocated area of storage media. However, this approach is often insufficient for dashcams that use pre-allocation strategies, where fixed-size files are created before recording. As a result, residual data from previous recordings may remain in unused portions of these allocated files but are typically overlooked during forensic investigations. In this study, we identify and define a forensic artifact termed "video file slack," which exists within the allocated space of pre-generated files and contains valid video data from previous recording cycles. To our knowledge, this study is among the first to investigate video recovery from slack regions created by dashcam pre-allocation mechanisms. We propose a method to extract the slack area from common container formats (AVI, MP4, and ASF) and reconstruct fragmented H.264 video streams using frame-based recovery techniques. Experimental results show that the proposed method expands the recoverable area by up to 966.7% compared with traditional analysis limited to the unallocated area. A hit-and-run case study further demonstrates that frames capturing the moment of impact can be recovered from video file slack even when no data are recovered from the unallocated area. These findings indicate that Video File Slack is a valuable source of forensic evidence in dashcam storage systems and that slack-based recovery can improve the completeness of digital forensic investigations.

Journal of Forensic Sciences
Kangwon National University (KR), National Forensic Institute (KR)
Openalex Percentile: Top 3%
Digital and Cyber Forensics
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