Hiding Local Manipulations on SAR Images: a Counter-Forensic Attack
- URL: http://arxiv.org/abs/2407.07041v1
- Date: Tue, 9 Jul 2024 17:03:57 GMT
- Title: Hiding Local Manipulations on SAR Images: a Counter-Forensic Attack
- Authors: Sara Mandelli, Edoardo Daniele Cannas, Paolo Bestagini, Stefano Tebaldini, Stefano Tubaro,
- Abstract summary: The vast accessibility of Synthetic Aperture Radar (SAR) images through online portals has propelled the research across various fields.
Vulnerability is further emphasized by the fact that most SAR products, despite their original complex nature, are often released as amplitude-only information.
In this paper we demonstrate that an expert practitioner can exploit the complex nature of SAR data to obscure any signs of manipulation within a locally altered amplitude image.
- Score: 17.78894837783128
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The vast accessibility of Synthetic Aperture Radar (SAR) images through online portals has propelled the research across various fields. This widespread use and easy availability have unfortunately made SAR data susceptible to malicious alterations, such as local editing applied to the images for inserting or covering the presence of sensitive targets. Vulnerability is further emphasized by the fact that most SAR products, despite their original complex nature, are often released as amplitude-only information, allowing even inexperienced attackers to edit and easily alter the pixel content. To contrast malicious manipulations, in the last years the forensic community has begun to dig into the SAR manipulation issue, proposing detectors that effectively localize the tampering traces in amplitude images. Nonetheless, in this paper we demonstrate that an expert practitioner can exploit the complex nature of SAR data to obscure any signs of manipulation within a locally altered amplitude image. We refer to this approach as a counter-forensic attack. To achieve the concealment of manipulation traces, the attacker can simulate a re-acquisition of the manipulated scene by the SAR system that initially generated the pristine image. In doing so, the attacker can obscure any evidence of manipulation, making it appear as if the image was legitimately produced by the system. We assess the effectiveness of the proposed counter-forensic approach across diverse scenarios, examining various manipulation operations. The obtained results indicate that our devised attack successfully eliminates traces of manipulation, deceiving even the most advanced forensic detectors.
Related papers
- ID-Guard: A Universal Framework for Combating Facial Manipulation via Breaking Identification [60.73617868629575]
misuse of deep learning-based facial manipulation poses a potential threat to civil rights.
To prevent this fraud at its source, proactive defense technology was proposed to disrupt the manipulation process.
We propose a novel universal framework for combating facial manipulation, called ID-Guard.
arXiv Detail & Related papers (2024-09-20T09:30:08Z) - Deep Learning Based Speckle Filtering for Polarimetric SAR Images. Application to Sentinel-1 [51.404644401997736]
We propose a complete framework to remove speckle in polarimetric SAR images using a convolutional neural network.
Experiments show that the proposed approach offers exceptional results in both speckle reduction and resolution preservation.
arXiv Detail & Related papers (2024-08-28T10:07:17Z) - On the Vulnerability of DeepFake Detectors to Attacks Generated by
Denoising Diffusion Models [0.5827521884806072]
We investigate the vulnerability of single-image deepfake detectors to black-box attacks created by the newest generation of generative methods.
Our experiments are run on FaceForensics++, a widely used deepfake benchmark consisting of manipulated images.
Our findings indicate that employing just a single denoising diffusion step in the reconstruction process of a deepfake can significantly reduce the likelihood of detection.
arXiv Detail & Related papers (2023-07-11T15:57:51Z) - MMNet: Multi-Collaboration and Multi-Supervision Network for Sequential
Deepfake Detection [81.59191603867586]
Sequential deepfake detection aims to identify forged facial regions with the correct sequence for recovery.
The recovery of forged images requires knowledge of the manipulation model to implement inverse transformations.
We propose Multi-Collaboration and Multi-Supervision Network (MMNet) that handles various spatial scales and sequential permutations in forged face images.
arXiv Detail & Related papers (2023-07-06T02:32:08Z) - Splicing Detection and Localization In Satellite Imagery Using
Conditional GANs [26.615687071827576]
We describe the use of a Conditional Generative Adversarial Network (cGAN) to identify spliced forgeries within satellite images.
Our method achieves high success on these detection and localization objectives.
arXiv Detail & Related papers (2022-05-03T22:25:48Z) - Exploring Frequency Adversarial Attacks for Face Forgery Detection [59.10415109589605]
We propose a frequency adversarial attack method against face forgery detectors.
Inspired by the idea of meta-learning, we also propose a hybrid adversarial attack that performs attacks in both the spatial and frequency domains.
arXiv Detail & Related papers (2022-03-29T15:34:13Z) - Transformer-based SAR Image Despeckling [53.99620005035804]
We introduce a transformer-based network for SAR image despeckling.
The proposed despeckling network comprises of a transformer-based encoder which allows the network to learn global dependencies between different image regions.
Experiments show that the proposed method achieves significant improvements over traditional and convolutional neural network-based despeckling methods.
arXiv Detail & Related papers (2022-01-23T20:09:01Z) - Amplitude SAR Imagery Splicing Localization [17.075910584827568]
This paper investigates the problem of amplitude SAR imagery splicing localization.
We leverage a Convolutional Neural Network (CNN) to extract a fingerprint highlighting inconsistencies in the processing traces of the analyzed input.
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.
arXiv Detail & Related papers (2022-01-07T11:42:09Z) - Detect and Locate: A Face Anti-Manipulation Approach with Semantic and
Noise-level Supervision [67.73180660609844]
We propose a conceptually simple but effective method to efficiently detect forged faces in an image.
The proposed scheme relies on a segmentation map that delivers meaningful high-level semantic information clues about the image.
The proposed model achieves state-of-the-art detection accuracy and remarkable localization performance.
arXiv Detail & Related papers (2021-07-13T02:59:31Z) - Generative Autoregressive Ensembles for Satellite Imagery Manipulation
Detection [18.977376778727898]
Satellite imagery is becoming increasingly accessible due to the growing number of orbiting commercial satellites.
Images can be easily tampered and modified with image manipulation tools damaging downstream applications.
In this paper, we use ensembles of generative autoregressive models to model the distribution of the pixels of the image in order to detect potential manipulations.
arXiv Detail & Related papers (2020-10-08T04:41:30Z) - Adversarial Attack on Deep Learning-Based Splice Localization [14.669890331986794]
Using a novel algorithm we demonstrate on three non end-to-end deep learning-based splice localization tools that hiding manipulations of images is feasible via adversarial attacks.
We find that the formed adversarial perturbations can be transferable among them regarding the deterioration of their localization performance.
arXiv Detail & Related papers (2020-04-17T20:31:38Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.