Abstract
Nowadays, smartphones are becoming the predominant source of video content that is being widely shared through various platforms of social media. According to recent studies, 78% of iPhones and 57% of android smartphones support hardware-accelerated HEVC decoding. Growing usage of videos on social media applications poses the challenge of distinguishing between authentic and manipulated content. In this paper, we have proposed the development of HEVC based Tampered Video Dataset (HTVD) consisting of diverse scenarios of authentic and forged videos for more comprehensive testing capabilities. The dataset will provide the researchers with a benchmark with a varied range of realistic and smartly tampered videos for validation and comparison of their forensic investigation techniques. The HTVD dataset is developed from videos captured under scenarios of indoor, outdoor, and surveillance shots. It includes 60 original videos, 966 tampered videos, their corresponding ground truth information, and masks. All the original videos are captured with HEVC supported smartphones. Various types of inter-frame forgeries such as frame insertion, frame deletion and frame duplication, and object-based intra-frame forgeries such as cloning, splicing, and inpainting are incorporated to create a diversified database of forged videos. Further, the tampered videos are provided with variations based on the encoding parameters of the codec namely, GOP size, CRF and frame types resulting in a total of 8,694 forged videos. To perform video tampering, the forger needs to perform recompression. However, videos may also undergo recompression while transferring over the internet or exchanged through social media applications. Thus, recompression doesn’t always mean that forgery has been performed. The proposed HTVD dataset provides a video dataset for experimenting with both circumstances. This video dataset is publicly available on https://drive.google.com/drive/folders/143NEyVjcHNVDDZVzIZm6nTEUk5-ezWb5?usp=sharing.
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Data Availability
The video dataset generated during the current study is available in Google Drive, https://drive.google.com/drive/folders/143NEyVjcHNVDDZVzIZm6nTEUk5-ezWb5?usp=sharingrepository
References
Aghamaleki JA, Behrad A (2016) Inter-frame video forgery detection and localization using intrinsic effects of double compression on quantization errors of video coding. Signal Process Image Commun 47:289–302
Aghamaleki JA, Behrad A (2016) Malicious inter-frame video tampering detection in mpeg videos using time and spatial domain analysis of quantization effects. Multimed Tools Appl 76:20691–20717
Akhtar N, Saddique M, Asghar K, Bajwa UI, Hussain M, Habib Z (2022) Digital video tampering detection and localization: review, representations, challenges and algorithm. Mathematics
Al-Sanjary OI, Ahmed AA, Sulong G (2016) Development of a video tampering dataset for forensic investigation. Forensic Sci Int 266:565–572
Amerini I, Galteri L, Caldelli R, Bimbo A (2019) Deepfake video detection through optical flow based cnn. 2019 IEEE/CVF international conference on computer vision workshop (ICCVW), pp 1205–1207
Ardizzone E, Mazzola G (2015) A tool to support the creation of datasets of tampered videos, vol 9280, pp 665–675. https://doi.org/10.1007/978-3-319-23234-8_61
Arnab A, Torr PHS (2017) Pixelwise instance segmentation with a dynamically instantiated network. 2017 IEEE conference on computer vision and pattern recognition, (CVPR), pp 879–888
Bai M, Urtasun R (2016) Deep watershed transform for instance segmentation. CoRR arXiv:1611.08303
Bakas J, Naskar R, Bakshi S (2021) Detection and localization of inter-frame forgeries in videos based on macroblock variation and motion vector analysis. Comput Electr Eng 89:106929
Bakas J, Naskar R, Dixit R (2018) Detection and localization of inter-frame video forgeries based on inconsistency in correlation distribution between haralick coded frames. Multimed Tools Appl 78:4905–4935
Bestagini P, Milani S, Tagliasacchi M, Tubaro S (2013) Local tampering detection in video sequences. 2013 IEEE 15th international workshop on multimedia signal processing (MMSP), pp 488–493
Bradski G (2000) The openCV library. Dr Dobb’s Journal of Software Tools
CANTATA Dataset (2013) http://www.hitech-projects.com/euprojects/cantata/datasets_cantata/dataset.html. Accessed 30 Jul 2020
Chen X, Dong C, Ji J, Cao J, Li X (2021) Image manipulation detection by multi-view multi-scale supervision. 2021 IEEE/CVF international conference on computer vision (ICCV), pp 14165–14173
Chen S, Tan S, Li B, Huang J (2016) Automatic detection of object-based forgery in advanced video. IEEE Trans Circuits Syst Video Technol 26:2138–2151
Chen H, Wo Y, Han G (2017) Multi-granularity geometrically robust video hashing for tampering detection. Multimed Tools Appl 77:5303–5321
Cozzolino G, Poggi L, Verdoliva D (2019) Extracting camera-based fingerprints for video forensics. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops
Cuevas C, Yáñez EM, García N (2016) Labeled dataset for integral evaluation of moving object detection algorithms: lasiesta. Comput Vis Image Underst 152:103–117
D’Avino D, Cozzolino D, Poggi G, Verdoliva L (2017) Autoencoder with recurrent neural networks for video forgery detection. arXiv:https://axiv.org/abs/1708.08754
Dai J, He K, Sun J (2016) Instance-aware semantic segmentation via multi-task network cascades. 2016 IEEE conference on computer vision and pattern recognition (CVPR), pp 3150–3158
Elrowayati AA, Abdullah MFL, Manaf AA, Alfagi AS (2017) Tampering detection of double-compression with the same quantization parameter in hevc video streams. 2017 7th IEEE international conference on control system, computing and engineering (ICCSCE), pp 174–179
FVD Dataset (2020) https://drive.google.com/drive/folders/1ryMNJvKDaa7y187O1Y1CEjr4FxTSsVq9. Accessed 10 March 2022
Fadl SM, Han Q, Li Q (2018) Authentication of surveillance videos: detecting frame duplication based on residual frame. J Forensic Sci, vol 63
Fadl SM, Han Q, Li Q (2019) Inter-frame forgery detection based on differential energy of residue. IET Image Process 13:522–528
Fadl SM, Han Q, Qiong L (2020) Exposing video inter-frame forgery via histogram of oriented gradients and motion energy image. Multidim Syst Sign Process:1–20
Fang Q, Jiang X, Sun T, Xu Q, Xu K (2019) Detection of hevc double compression with different quantization parameters based on property of dct coefficients and tus. 2019 12th international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI), pp 1–6
Fayyaz M, Anjum A, Ziauddin S, Khan A, Sarfaraz A (2019) An improved surveillance video forgery detection technique using sensor pattern noise and correlation of noise residues. Multimed Tools Appl 79:5767–5788
GRIP Dataset (2017) http://www.grip.unina.it/web-download.html. Accessed 3 Aug 2020
Garcia-Garcia A, Orts S, Oprea S, Villena-Martinez V, Martinez-Gonzalez P, Rodríguez JG (2018) A survey on deep learning techniques for image and video semantic segmentation. Appl Soft Comput 70:41–65
Geng Q, Zhou Z, Cao X (2017) Survey of recent progress in semantic image segmentation with cnns. Sci China Inf Sci 61:1–18
Guo Y, Liu Y, Georgiou T, Lew MS (2017) A review of semantic segmentation using deep neural networks. Int J Multimed Inf Retrieval 7:87–93
He K, Gkioxari G, Dollár P, Girshick RB (2020) . Mask r-cnn IEEE Trans Pattern Anal Mach Intell 42:386–397
He P, Li H, Wang H, Wang S, Jiang X, Zhang R (2021) Frame-wise detection of double hevc compression by learning deep spatio-temporal representations in compression domain. IEEE Trans Multimed 23:3179–3192
Hong JH, Yang Y, Oh BT (2019) Detection of frame deletion in hevc-coded video in the compressed domain. Digit Investig 30:23–31
Ilan S, Shamir A (2015) A survey on data-driven video completion. Comput Graph Forum, vol 34
Javed AR, Jalil Z, Zehra W, Gadekallu TR, Suh DY, Piran MJ (2021) A comprehensive survey on digital video forensics: taxonomy, challenges, and future directions. Eng Appl Artif Intell 106:104456
Jia S, Xu Z, Wang H, Feng C, Wang T (2018) Coarse-to-fine copy-move forgery detection for video forensics. IEEE Access 6:25323–25335
Johnston P, Elyan E (2019) A review of digital video tampering: from simple editing to full synthesis. Digit Investig 29:67–81
Kaur H, Jindal N (2020) Image and video forensics: a critical survey. Wirel Pers Commun 112:1281–1302
Kingra S, Aggarwal N, Singh RD (2016) Video inter-frame forgery detection: a survey. Indian J Sci Technol, vol 9
Kobayashi M, Okabe T, Sato Y (2010) Detecting forgery from static-scene video based on inconsistency in noise level functions. IEEE Trans Inf Forensics Security 5:883–892
Lateef F, Ruichek Y (2019) Survey on semantic segmentation using deep learning techniques. Neurocomputing 338:321–348
Le TT, Almansa A, Gousseau Y, Masnou S (2017) Motion-consistent video inpainting. 2017 IEEE international conference on image processing (ICIP), pp 2094–2098
Panchal HD, Shah HB (2020) Video tampering dataset development in temporal domain for video forgery authentication. Multimed Tools Appl:1–25
Qadir G, Yahaya S, Ho ATS (2012) Surrey university library for forensic analysis (sulfa) of video content. In: IET conference on image processing (IPR 2012), pp 1–6
Richao C, Gaobo Y, Ningbo Z (2014) Detection of object-based manipulation by the statistical features of object contour. Forensic Sci Int 236:164–9
Rössler A, Cozzolino D, Verdoliva L, Riess C, Thies J, NieSS M (2019) Faceforensics++: learning to detect manipulated facial images. 2019 IEEE/CVF international conference on computer vision (ICCV), pp 1–11
Ruiz-Santaquitaria J, Bueno G, Déniz-Suárez O, Vállez N, Cristóbal G (2020) Semantic versus instance segmentation in microscopic algae detection. Eng Appl Artif Intell, vol 87
SULFA Dataset (2012) http://sulfa.cs.surrey.ac.uk/forged.php. Accessed 8 Jul 2020
Sharma H, Kanwal N (2021) Video interframe forgery detection: classification, technique & new dataset. J Comput Secur 29:531–550
Shelke NA, Kasana SS (2021) A comprehensive survey on passive techniques for digital video forgery detection. Multimed Tools Appl 80:6247–6310
Shullani D, Fontani M, Iuliani M, Shaya OA, Piva A (2017) Vision: a video and image dataset for source identification. EURASIP J Inf Security 2017:1–16
Singh RD, Aggarwal N (2017) Video content authentication techniques: a comprehensive survey. Multimed Syst 24:211–240
Singh RD, Aggarwal N (2017) Optical flow and prediction residual based hybrid forensic system for inter-frame tampering detection. J Circuits Syst Comput 26:1750107–1175010737
Sitara K, Mehtre BM (2018) Detection of inter-frame forgeries in digital videos. Forensic Sci Int 289:186–206
Sohn H, Neve WD, Ro YM (2011) Privacy protection in video surveillance systems: analysis of subband-adaptive scrambling in jpeg xr. IEEE Trans Circuits Syst Video Technol 21:170–177
Sullivan GJ, Ohm J-R, Han W, Wiegand T (2012) Overview of the high efficiency video coding (hevc) standard. IEEE Trans Circuits Syst Video Technol 22:1649–1668
TVD Dataset (2015) https://drive.google.com/file/d/0B0f6ko6Ln2C3XzJQemZZZjNKSjQ/view?usp=sharing. Accessed 20 Jul 2020
Test Database (2017) http://ceng2.ktu.edu.tr/%7Egulutas/test_database.rar. Accessed 15 March 2022
Tomar S (2006) Converting video formats with ffmpeg. Linux J 2006(146):10
Ulutas G, Ustubioglu B, Ulutas M, Nabiyev VV (2017) Frame duplication detection based on bow model. Multimed Syst 24:549–567
VTD Dataset (2016) https://www.youtube.com/channel/UCZuuu-iyZvPptbIUHT9tMrA. Accessed 30 Jul 2020
Video: copy-move forgeries dataset - REWIND project (2013) https://sites.google.com/site/rewindpolimi/downloads/datasets/video-copy-move-forgeries-dataset. Accessed 8 Jul 2020
Wang Q, Li Z, Zhang Z, Ma Q (2014) Video inter-frame forgery identification based on consistency of correlation coefficients of gray values. J Comput Chem 02:51–57
Wang Q, ZH L, Zhang Z, QL M (2014) Video inter-frame forgery identification based on optical flow consistency. Sensors Trans 166:229–234
Wei W, Fan X, Song H, Wang H (2017) Video tamper detection based on multi-scale mutual information. Multimed Tools Appl:1–18
Wu Y, AbdAlmageed W, Natarajan P (2019) Mantra-net: manipulation tracing network for detection and localization of image forgeries with anomalous features. In: 2019 IEEE/CVF conference on computer vision and pattern recognition (CVPR), pp 9535–9544
Yao Y, Shi YQ, Weng S, Guan B (2018) Deep learning for detection of object-based forgery in advanced video. Symmetry 10:3
Yu T, Li W, Li X, Lu J, Zhou J (2021) Frequency-aware spatiotemporal transformers for video inpainting detection. 2021 IEEE/CVF international conference on computer vision (ICCV), pp 8168–8177
Zeng Y, Fu J, Chao H (2020) Learning joint spatial-temporal transformations for video inpainting. arXiv:2007.10247
Zheng L, Sun T, Shi YQ (2014) Inter-frame video forgery detection based on block-wise brightness variance descriptor. In: IWDW
Zhong J, Gan Y, Young J, Huang L, Lin P (2016) A new block-based method for copy move forgery detection under image geometric transforms. Multimed Tools Appl 76:14887–14903
Zhong J, Pun C-M, Gan Y (2020) Dense moment feature index and best match algorithms for video copy-move forgery detection. Inf Sci 537:184–202
Zhuo L, Tan S, Li B, Huang J (2022) Self-adversarial training incorporating forgery attention for image forgery localization. IEEE Trans Inf Forensics Security 17:819–834
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Singla, N., Singh, J., Nagpal, S. et al. HEVC based tampered video database development for forensic investigation. Multimed Tools Appl 82, 25493–25526 (2023). https://doi.org/10.1007/s11042-022-14303-y
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DOI: https://doi.org/10.1007/s11042-022-14303-y