Are classic forensic tools effective on satellite imagery?
| Autor: | Matthieu, Serfaty, Tina, Nikoukhah, Quentin, Bammey, Von Gioi, Grompone, De Franchis |
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| Jazyk: | angličtina |
| Rok vydání: | 2023 |
| Předmět: | |
| DOI: | 10.5281/zenodo.8119273 |
| Popis: | Satellite images are becoming an increasingly important part of our world. Such images are used to forecast the weather, track green house gas emissions, monitor agricultural crop health, and many other applications. Such advances are possible thanks to the free availability of a large number of satellite images. Satellite imagery now plays a key role in many areas, including external security. In this context, it is necessary to question the reliability of this data. Can the authenticity of a satellite image be guaranteed? How can one protect oneself against an entity wishing to hide illegal military material or, conversely, to incite action against another entity by falsely suggesting that it possess such material? If the forensic analysis of photographs has attracted a great deal of academic interest in recent years, this is not yet the case for satellite imagery. In this paper, we propose a methodology to create a very simple but interesting dataset to test the performance of state-of-the-art forensic methods on pristine and manipulated satellite images. Despite the strong performance of such algorithms, satellite images require special attention due to the nature of the images themselves. This work has received funding by the European Union under the Horizon Europe vera.ai project, Grant Agreement number 101070093, and from ANR under APATE project, grant ANR-22-CE39-0016. Centre Borelli is also a member of Université Paris Cité, SSA and INSERM. Supported by PHD Grant DGA/AID (No 01D22020572) {"references":["S. Kalyan Yarlagadda, D. G ̈uera, P. Bestagini, FM Zhu, S. Tubaro, and EJ Delp, \"Satellite image forgery detec- tion and localization using gan and one-class classifier,\" arXiv e-prints, 2018",". Zhan, D. Hu, Y. Wang, and X. Yu, \"Semisupervised hyperspectral image classification based on generative adversarial networks,\" IEEE Geoscience and Remote Sensing Letters, vol. 15, no. 2, pp. 212–216, 2018.","L. Abady, M. Barni, A. Garzelli, and B. Tondi, \"Gan generation of synthetic multispectral satellite images,\" in Image and Signal Processing for Remote Sensing XXVI. SPIE, 2020","L. Wang, H. Lu, Y. Wang, M. Feng, D. Wang, B. Yin, and X. Ruan, \"Learning to detect salient objects with image-level supervision,\" in IEEE/CVF CVPR, 2017.","D. Cozzolino, G. Poggi, and L. Verdoliva, \"Splice- buster: A new blind image splicing detector,\" in 2015 IEEE WIFS, 2015.",". Wu, W. AbdAlmageed, and P. Natarajan, \"Mantra- net: Manipulation tracing network for detection and lo- calization of image forgeries with anomalous features,\" in IEEE/CVF CVPR, 2019.","Q. Bammey, R. Grompone von Gioi, and J.-M. Morel, \"An adaptive neural network for unsupervised mosaic consistency analysis in image forensics,\" CVPR, 2020.",". Nikoukhah, J. Anger, M. Colom, J.-M. Morel, and R. Grompone von Gioi, \"ZERO: a Local JPEG Grid Origin Detector Based on the Number of DCT Zeros and its Applications in Image Forensics,\" IPOL, vol. 11, pp. 396–433, 2021.","M. Gardella, P. Mus ́e, J.-M. Morel, and M. Colom, \"Noisesniffer: a fully automatic image forgery detector based on noise analysis,\" in 2021 IEEE IWBF, 2021.","D. Cozzolino and L. Verdoliva, \"Noiseprint: A cnn- based camera model fingerprint,\" IEEE TIFS, vol. 15, pp. 144–159, 2019.","ER Bartusiak, SK Yarlagadda, D. G ̈uera, P. Bestagini, S. Tubaro, FM Zhu, and EJ Delp, \"Splicing detection and localization in satellite imagery using conditional gans,\" in 2019 IEEE MIPR, 2019","J. Horv ́ath, DM Montserrat, H. Hao, and EJ Delp, \"Ma- nipulation detection in satellite images using deep belief networks,\" in IEEE/CVF CVPR Workshops, 2020","J. Horv ́ath, S. Baireddy, H. Hao, DM Montserrat, and EJ Delp, \"Manipulation detection in satellite images us- ing vision transformer,\" in IEEE/CVF CVPR, 2021.","S. Mohajerani, T. A. Krammer, and P. Saeedi, \"A cloud detection algorithm for remote sensing images using fully convolutional neural networks,\" in 2018 IEEE 20th International Workshop on MMSP, 2018.","D. Saini, \"Satellite images for computer vision tasks,\" 2022, Kaggle","D. Chicco and G. Jurman, \"The advantages of the matthews correlation coefficient (mcc) over f1 score and accuracy in binary classification evaluation,\" BMC ge- nomics, 2020."]} |
| Databáze: | OpenAIRE |
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