Amplitude SAR Imagery Splicing Localization

Synthetic Aperture Radar (SAR) images are a valuable asset for a wide variety of tasks. In the last few years, many websites have been offering them for free in the form of easy to manage products, favoring their widespread diffusion and research work in the SAR field. The drawback of these opportunities is that such images might be exposed to forgeries and manipulations by malicious users, raising new concerns about their integrity and trustworthiness. Up to now, the multimedia forensics literature has proposed various techniques to localize manipulations in natural photographs, but the integrity assessment of SAR images was never investigated. This task poses new challenges, since SAR images are generated with a processing chain completely different from that of natural photographs. This implies that many forensics methods developed for natural images are not guaranteed to succeed. In this paper, we investigate the problem of amplitude SAR imagery splicing localization. Our goal is to localize regions of an amplitude SAR image that have been copied and pasted from another image, possibly undergoing some kind of editing in the process. To do so, we leverage a Convolutional Neural Network (CNN) to extract a fingerprint highlighting inconsistencies in the processing traces of the analyzed input. Then, we examine this fingerprint to produce a binary tampering mask indicating the pixel region under splicing attack. Results show that our proposed method, tailored to the nature of SAR signals, provides better performances than state-of-the-art forensic tools developed for natural images.

[1]  Paheding Sidike,et al.  U-Net and its variants for medical image segmentation: theory and applications , 2020, ArXiv.

[2]  Bin Cui,et al.  An Efficient Change Detection for Large SAR Images Based on Modified U-Net Framework , 2020 .

[3]  Haipeng Wang,et al.  Target Classification Using the Deep Convolutional Networks for SAR Images , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[4]  Ross B. Girshick,et al.  Focal Loss for Dense Object Detection , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Fabio Remondino,et al.  The Point Where Reality Meets Fantasy: Mixed Adversarial Generators for Image Splice Detection , 2019, NeurIPS.

[6]  A. Maalej,et al.  Robust Concurrent Detection of Salt Domes and Faults in Seismic Surveys Using an Improved UNet Architecture , 2022, IEEE Access.

[7]  Paolo Bestagini,et al.  Training CNNs in Presence of JPEG Compression: Multimedia Forensics vs Computer Vision , 2020, 2020 IEEE International Workshop on Information Forensics and Security (WIFS).

[8]  Lei Zhang,et al.  Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising , 2016, IEEE Transactions on Image Processing.

[9]  Davide Cozzolino,et al.  Splicebuster: A new blind image splicing detector , 2015, 2015 IEEE International Workshop on Information Forensics and Security (WIFS).

[10]  David Vazquez-Padin,et al.  Prefilter design for forensic resampling estimation , 2011, 2011 IEEE International Workshop on Information Forensics and Security.

[11]  Xiao Xiang Zhu,et al.  SEN12MS - A Curated Dataset of Georeferenced Multi-Spectral Sentinel-1/2 Imagery for Deep Learning and Data Fusion , 2019, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences.

[12]  Paolo Bestagini,et al.  Satellite Image Forgery Detection and Localization Using GAN and One-Class Classifier , 2018, Media Watermarking, Security, and Forensics.

[13]  Davide Cozzolino,et al.  Single-image splicing localization through autoencoder-based anomaly detection , 2016, 2016 IEEE International Workshop on Information Forensics and Security (WIFS).

[14]  Miroslav Goljan,et al.  Digital camera identification from sensor pattern noise , 2006, IEEE Transactions on Information Forensics and Security.

[15]  Paolo Bestagini,et al.  Open-Set Source Attribution for Panchromatic Satellite Imagery , 2021, 2021 IEEE International Conference on Image Processing (ICIP).

[16]  Vincenzo Lipari,et al.  Multiple Jpeg Compression Detection Through Task-Driven Non-Negative Matrix Factorization , 2018, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[17]  Xing Zhang,et al.  Exposing Region Splicing Forgeries with Blind Local Noise Estimation , 2013, International Journal of Computer Vision.

[18]  Segmentation Models , 2016, Brand Management Strategies.

[19]  I. Hajnsek,et al.  A tutorial on synthetic aperture radar , 2013, IEEE Geoscience and Remote Sensing Magazine.

[20]  Paolo Bestagini,et al.  Tampering Detection and Localization Through Clustering of Camera-Based CNN Features , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[21]  Alessandro Piva,et al.  Detection of Nonaligned Double JPEG Compression Based on Integer Periodicity Maps , 2012, IEEE Transactions on Information Forensics and Security.

[22]  Hany Farid,et al.  Exposing digital forgeries by detecting traces of resampling , 2005, IEEE Transactions on Signal Processing.

[23]  Yao Zhao,et al.  Forensic detection of median filtering in digital images , 2010, 2010 IEEE International Conference on Multimedia and Expo.

[24]  Edward J. Delp,et al.  Generative Autoregressive Ensembles for Satellite Imagery Manipulation Detection , 2020, 2020 IEEE International Workshop on Information Forensics and Security (WIFS).

[25]  Florent Retraint,et al.  JPEG Quantization Step Estimation and Its Applications to Digital Image Forensics , 2017, IEEE Transactions on Information Forensics and Security.

[26]  Paolo Bestagini,et al.  Anomaly-Based Manipulation Detection in Satellite Images , 2019, CVPR Workshops.

[27]  Anthony Tung Shuen Ho,et al.  A semi-fragile pinned sine transform watermarking system for content authentication of satellite images , 2005, Proceedings. 2005 IEEE International Geoscience and Remote Sensing Symposium, 2005. IGARSS '05..

[28]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[29]  Xianfeng Zhao,et al.  Image Forgery Localization based on Multi-Scale Convolutional Neural Networks , 2018, IH&MMSec.

[30]  Quoc V. Le,et al.  EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks , 2019, ICML.

[31]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[32]  Jessica Fridrich,et al.  Improving EfficientNet for JPEG Steganalysis , 2021, IH&MMSec.

[33]  Hiroki Nakahara,et al.  Identification of inpainted satellite images using evalutionary artificial neural network (EANN) and k-nearest neighbor(KNN) algorithm , 2017, 2017 8th International Conference of Information and Communication Technology for Embedded Systems (IC-ICTES).

[34]  B. Tondi,et al.  GAN generation of synthetic multispectral satellite images , 2020, Remote Sensing.

[35]  Paolo Bestagini,et al.  Video Face Manipulation Detection Through Ensemble of CNNs , 2020, 2020 25th International Conference on Pattern Recognition (ICPR).

[36]  K. Tomiyasu,et al.  Tutorial review of synthetic-aperture radar (SAR) with applications to imaging of the ocean surface , 1978, Proceedings of the IEEE.

[37]  Jessica J. Fridrich,et al.  On detection of median filtering in digital images , 2010, Electronic Imaging.

[38]  Xiao Xiang Zhu,et al.  The SEN1-2 Dataset for Deep Learning in SAR-Optical Data Fusion , 2018, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences.

[39]  Belhassen Bayar,et al.  A Deep Learning Approach to Universal Image Manipulation Detection Using a New Convolutional Layer , 2016, IH&MMSec.

[40]  Francesco De Zan,et al.  TOPSAR: Terrain Observation by Progressive Scans , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[41]  Davide Cozzolino,et al.  Noiseprint: A CNN-Based Camera Model Fingerprint , 2018, IEEE Transactions on Information Forensics and Security.

[42]  James Hays,et al.  Localizing and Orienting Street Views Using Overhead Imagery , 2016, ECCV.

[43]  Yang Wei,et al.  RRU-Net: The Ringed Residual U-Net for Image Splicing Forgery Detection , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[44]  Qingjie Liu,et al.  Road Extraction by Deep Residual U-Net , 2017, IEEE Geoscience and Remote Sensing Letters.

[45]  Shengjun Liu,et al.  Learning to predict crisp boundaries , 2018, ECCV.

[46]  Antonio J. Plaza,et al.  Image Segmentation Using Deep Learning: A Survey , 2021, IEEE transactions on pattern analysis and machine intelligence.

[47]  Paolo Bestagini,et al.  Splicing Detection and Localization In Satellite Imagery Using Conditional GANs , 2019, 2019 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR).

[48]  J. MacQueen.  Some methods for classification and analysis of multivariate observations , 1967 .

[49]  Paolo Bestagini,et al.  Fooling PRNU-Based Detectors Through Convolutional Neural Networks , 2018, 2018 26th European Signal Processing Conference (EUSIPCO).

[50]  Luisa Verdoliva.  Deep Learning in Multimedia Forensics , 2018, IH&MMSec.

[51]  F. Henderson,et al.  Principles and Applications of Imaging Radar , 1998 .

[52]  Matthias Kirchner,et al.  Fast and reliable resampling detection by spectral analysis of fixed linear predictor residue , 2008, MM&Sec '08.

[53]  Matthias Kirchner,et al.  Unsupervised Image Manipulation Localization With Non-Binary Label Attribution , 2019, IEEE Signal Processing Letters.

[54]  Min Wu,et al.  Information Forensics: An Overview of the First Decade , 2013, IEEE Access.

[55]  Belhassen Bayar,et al.  Design Principles of Convolutional Neural Networks for Multimedia Forensics , 2017, Media Watermarking, Security, and Forensics.

[56]  Jie Geng,et al.  High-Resolution SAR Image Classification via Deep Convolutional Autoencoders , 2015, IEEE Geoscience and Remote Sensing Letters.

[57]  Chengbin Deng,et al.  Deep fake geography? When geospatial data encounter Artificial Intelligence , 2021 .

[58]  Jiangqun Ni,et al.  A deep learning approach to detection of splicing and copy-move forgeries in images , 2016, 2016 IEEE International Workshop on Information Forensics and Security (WIFS).

[59]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.