SpoC: Spoofing Camera Fingerprints
Open accessAbstract
Thanks to the fast progress in synthetic media generation, creating realistic false images has become very easy. Such images can be used to wrap rich fake news with enhanced credibility, spawning a new wave of high-impact, high-risk misinformation campaigns. Therefore, there is a fast-growing interest in reliable detectors of manipulated media. The most powerful detectors, to date, rely on the subtle traces left by any device on all images acquired by it. In particular, due to proprietary in-camera processes, like demosaicing or compression, each camera model leaves trademark traces that can be exploited for forensic analyses. The absence or distortion of such traces in the target image is a strong hint of manipulation. In this paper, we challenge such detectors to gain better insight into their vulnerabilities. This is an important study in order to build better forgery detectors able to face malicious attacks. Our proposal consists of a GAN-based approach that injects camera traces into synthetic images. Given a GAN-generated image, we insert the traces of a specific camera model into it and deceive state-of-the-art detectors into believing the image was acquired by that model. Likewise, we deceive independent detectors of synthetic GAN images into believing the image is real. Experiments prove the effectiveness of the proposed method in a wide array of conditions. Moreover, no prior information on the attacked detectors is needed, but only sample images from the target camera.
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Deep learning-based deepfake detectors are vulnerable to adversarial attacks, with detection performance degrading sharply even in black-box scenarios.
“It is also worth pointing out that deep learning-based methods are vulnerable to adversarial attacks with detection performance that degrades sharply even in a black-box scenario [13,27,45].”
From Audio-Visual Person-of-Interest DeepFake Detection (2023) · cites this paper as [13]
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Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works
Densely Connected Convolutional Networks
Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.243 · Cited by 48,566 · see citing works
Rethinking the Inception Architecture for Computer Vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.308 · Cited by 32,138 · see citing works
Unpaired image-to-image translation using cycle-consistent adversarial networks
Zhu, J.-Y., Park, T., Isola, P., & Efros, A. A. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.244 · Cited by 23,602 · see citing works
Image-to-Image Translation with Conditional Adversarial Networks
Isola, P., Zhu, J.-Y., Zhou, T., & Efros, A. A. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.632 · Cited by 22,930 · see citing works
Xception: Deep Learning with Depthwise Separable Convolutions
Chollet, F. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.195 · Cited by 20,380 · see citing works
FaceNet: A unified embedding for face recognition and clustering
Schroff, F., Kalenichenko, D., & Philbin, J. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298682 · Cited by 15,267 · see citing works
A Style-Based Generator Architecture for Generative Adversarial Networks
Karras, T., Laine, S., & Aila, T. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00453 · Cited by 11,714 · see citing works
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
Johnson, J., Alahi, A., & Fei-Fei, L. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46475-6_43 · Cited by 11,283 · see citing works
Towards Deep Learning Models Resistant to Adversarial Attacks
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., & Vladu, A. (2017) · arXiv · doi:10.48550/arxiv.1706.06083 · Cited by 6,602 · see citing works
Analyzing and Improving the Image Quality of StyleGAN
Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., & Aila, T. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00813 · Cited by 6,332 · see citing works
High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs
Wang, T.-C., Liu, M.-Y., Zhu, J.-Y., Tao, A., Kautz, J., & Catanzaro, B. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00917 · Cited by 4,514 · see citing works
StarGAN: Unified Generative Adversarial Networks for Multi-domain Image-to-Image Translation
Choi, Y., Choi, M., Kim, M., Ha, J.-W., Kim, S., & Choo, J. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00916 · Cited by 4,163 · see citing works
Large Scale GAN Training for High Fidelity Natural Image Synthesis
Brock, A., Donahue, J., & Simonyan, K. (2018) · arXiv · doi:10.48550/arxiv.1809.11096 · Cited by 3,717 · see citing works
Spectral Normalization for Generative Adversarial Networks
Miyato, T., Kataoka, T., Koyama, M., & Yoshida, Y. (2018) · arXiv · doi:10.48550/arxiv.1802.05957 · Cited by 3,438 · see citing works
FaceForensics++: Learning to Detect Manipulated Facial Images
Rossler, A., Cozzolino, D., Verdoliva, L., Riess, C., Thies, J., & Niessner, M. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00009 · Cited by 3,314 · see citing works
Self-citationFace2Face: Real-Time Face Capture and Reenactment of RGB Videos
Thies, J., Zollhofer, M., Stamminger, M., Theobalt, C., & Niessner, M. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.262 · Cited by 1,747 · see citing works
Self-citationDigital Camera Identification From Sensor Pattern Noise
Luka, J., Fridrich, J., & Goljan, M. (2006) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2006.873602 · Cited by 1,328 · see citing works
CNN-Generated Images Are Surprisingly Easy to Spot… for Now
Wang, S.-Y., Wang, O., Zhang, R., Owens, A., & Efros, A. A. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00872 · Cited by 1,210 · see citing works
Evading Defenses to Transferable Adversarial Examples by Translation-Invariant Attacks
Dong, Y., Pang, T., Su, H., & Zhu, J. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00444 · Cited by 1,034 · see citing works
Generating Adversarial Examples with Adversarial Networks
Xiao, C., Li, B., Zhu, J.-Y., He, W., Liu, M., & Song, D. X. (2018) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2018/543 · Cited by 988 · see citing works
Media Forensics and DeepFakes: An Overview
Verdoliva, L. (2020) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2020.3002101 · Cited by 979 · see citing works
Self-citationDetermining Image Origin and Integrity Using Sensor Noise
Chen, M., Fridrich, J., Goljan, M., & Lukas, J. (2008) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2007.916285 · Cited by 892 · see citing works
Exploiting Visual Artifacts to Expose Deepfakes and Face Manipulations
Matern, F., Riess, C., & Stamminger, M. (2019) · 2019 IEEE Winter Applications of Computer Vision Workshops (WACVW) · doi:10.1109/wacvw.2019.00020 · Cited by 824 · see citing works
On the Detection of Digital Face Manipulation
Dang, H., Liu, F., Stehouwer, J., Liu, X., & Jain, A. K. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00582 · Cited by 764 · see citing works
FSGAN: Subject Agnostic Face Swapping and Reenactment
Nirkin, Y., Keller, Y., & Hassner, T. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00728 · Cited by 706 · see citing works
Thies, J., Zollhöfer, M., Stamminger, M., Theobalt, C., & Nießner, M. (2016) · ACM SIGGRAPH Emerging Technologies · doi:10.1145/2929464.2929475 · Cited by 586 · see citing works
Self-citationDetecting and Simulating Artifacts in GAN Fake Images
Zhang, X., Karaman, S., & Chang, S.-F. (2019) · 2019 IEEE International Workshop on Information Forensics and Security (WIFS) · doi:10.1109/wifs47025.2019.9035107 · Cited by 567 · see citing works
Noiseprint: A CNN-Based Camera Model Fingerprint
Cozzolino, D., & Verdoliva, L. (2020) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2019.2916364 · Cited by 499 · see citing works
Self-citationImage Forgery Localization via Fine-Grained Analysis of CFA Artifacts
Ferrara, P., Bianchi, T., Rosa, A. D., & Piva, A. (2012) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2012.2202227 · Cited by 494 · see citing works
Novel Visual and Statistical Image Features for Microblogs News Verification
Jin, Z., Cao, J., Zhang, Y., Zhou, J., & Tian, Q. (2016) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2016.2617078 · Cited by 470 · see citing works
Detection of GAN-Generated Fake Images over Social Networks
Marra, F., Gragnaniello, D., Cozzolino, D., & Verdoliva, L. (2018) · 2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR) · doi:10.1109/mipr.2018.00084 · Cited by 399 · see citing works
Self-citationGenerative Adversarial Perturbations
Poursaeed, O., Katsman, I., Gao, B., & Belongie, S. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00465 · Cited by 370 · see citing works
Generating Adversarial Malware Examples for Black-Box Attacks Based on GANPreprint
Hu, W., & Tan, Y. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1702.05983 · Cited by 361 · see citing works
Do GANs leave artificial fingerprints?
Marra, F., Gragnaniello, D., Verdoliva, L., & Poggi, G. (2019) · 2019 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR) · doi:10.1109/mipr.2019.00103 · Cited by 361 · see citing works
Self-citationWhat Makes Fake Images Detectable? Understanding Properties that Generalize
Chai, L., Bau, D., Lim, S.-N., & Isola, P. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58574-7_7 · Cited by 357 · see citing works
First Steps Toward Camera Model Identification With Convolutional Neural Networks
Bondi, L., Baroffio, L., Güera, D., Bestagini, P., Delp, E. J., & Tubaro, S. (2016) · IEEE Signal Processing Letters · doi:10.1109/lsp.2016.2641006 · Cited by 275 · see citing works
Exposing Region Splicing Forgeries with Blind Local Noise Estimation
Lyu, S., Pan, X., & Zhang, X. (2013) · International Journal of Computer Vision · doi:10.1007/s11263-013-0688-y · Cited by 231 · see citing works
Constructing Unrestricted Adversarial Examples with Generative ModelsPreprint
Song, Y., Shu, R., Kushman, N., & Ermon, S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.07894 · Cited by 227 · see citing works
Camera model identification with the use of deep convolutional neural networks
Tuama, A., Comby, F., & Chaumont, M. (2016) · IEEE International Workshop on Information Forensics and Security · doi:10.1109/wifs.2016.7823908 · Cited by 226 · see citing works
Are GAN Generated Images Easy to Detect? A Critical Analysis of the State-Of-The-Art
Gragnaniello, D., Cozzolino, D., Marra, F., Poggi, G., & Verdoliva, L. (2021) · 2021 IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme51207.2021.9428429 · Cited by 212 · see citing works
Self-citationEvading Deepfake-Image Detectors with White- and Black-Box Attacks
Carlini, N., & Farid, H. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00337 · Cited by 170 · see citing works
AdvFaces: Adversarial Face Synthesis
Deb, D., Zhang, J., & Jain, A. K. (2020) · IEEE International Joint Conference on Biometrics (IJCB) · doi:10.1109/ijcb48548.2020.9304898 · Cited by 163 · see citing works
RSGAN: face swapping and editing using face and hair representation in latent spaces
Natsume, R., Yatagawa, T., & Morishima, S. (2018) · ACM SIGGRAPH 2018 Posters · doi:10.1145/3230744.3230818 · Cited by 140 · see citing works
Exposing GAN-synthesized Faces Using Landmark Locations
Yang, X., Li, Y., Qi, H., & Lyu, S. (2019) · ACM Workshop on Information Hiding and Multimedia Security (IH&MMSec) · doi:10.1145/3335203.3335724 · Cited by 127 · see citing works
Forensic Camera Model Identification
Kirchner, M., & Gloe, T. (2015) · doi:10.1002/9781118705773.ch9 · Cited by 73 · see citing works
A Counter-Forensic Method for CNN-Based Camera Model Identification
Güera, D., Wang, Y., Bondi, L., Bestagini, P., Tubaro, S., & Delp, E. J. (2017) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2017.230 · Cited by 73 · see citing works
Photo forensics from JPEG dimples
Agarwal, S., & Farid, H. (2017) · doi:10.1109/wifs.2017.8267641 · Cited by 73 · see citing works
Make a Face: Towards Arbitrary High Fidelity Face Manipulation
Qian, S., Lin, K.-Y., Wu, W., Liu, Y., Wang, Q., Shen, F., Qian, C., & He, R. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.01013 · Cited by 69 · see citing works
Adversarial Multimedia Forensics: Overview and Challenges Ahead
Barni, M., Stamm, M. C., & Tondi, B. (2018) · European Signal Processing Conference (EUSIPCO) · doi:10.23919/eusipco.2018.8553305 · Cited by 62 · see citing works
On the vulnerability of deep learning to adversarial attacks for camera model identification
Marra, F., Gragnaniello, D., & Verdoliva, L. (2018) · Signal Processing Image Communication · doi:10.1016/j.image.2018.04.007 · Cited by 58 · see citing works
Self-citationSynthesis of color filter array pattern in digital images
Kirchner, M., & Böhme, R. (2009) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.805988 · Cited by 57 · see citing works
Generative Adversarial Attacks Against Deep- Learning-Based Camera Model Identification
Chen, C., Zhao, X., & Stamm, M. C. (2019) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2019.2945198 · Cited by 56 · see citing works
On the Transferability of Adversarial Examples against CNN-based Image Forensics
Barni, M., Kallas, K., Nowroozi, E., & Tondi, B. (2019) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2019.8683772 · Cited by 43 · see citing works
Analysis of Adversarial Attacks against CNN-based Image Forgery Detectors
Gragnaniello, D., Marra, F., Poggi, G., & Verdoliva, L. (2018) · European Signal Processing Conference (EUSIPCO) · doi:10.23919/eusipco.2018.8553560 · Cited by 40 · see citing works
Self-citationAT-GAN: A Generative Attack Model for Adversarial Transferring on Generative Adversarial Nets.Preprint
Wang, X., He, K., & Hopcroft, J. E. (2019) · arXiv (Cornell University) · Cited by 23 · see citing works
Chen, C., Xiong, Z., Liu, X., & Wu, F. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00302 · Cited by 22 · see citing works
AT-GAN: An Adversarial Generator Model for Non-constrained Adversarial ExamplesPreprint
Wang, X., He, K., Song, C., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.07793 · Cited by 19 · see citing works
GANprintR: Improved Fakes and Evaluation of the State of the Art in Face Manipulation Detection
Neves, J. C., Tolosana, R., Vera-Rodriguez, R., Lopes, V., Proenca, H., & Fierrez, J. (2020) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2020.3007250 · Cited by 12 · see citing works
Flagged first, then by citations
Research works printed but not linked: 7
- [6]6. Andrew Brock, Jeff Donahue, and Karen Simonyan. Large scale GAN training for high fidelity natural image synthe- sis. In International Conference on Learning Representa- tions, 2019. 1
- [19]19. Ian Goodfellow, Jonathon Shlens, and Christian Szegedy. Explaining and harnessing adversarial examples. In Inter- national Conference on Learning Representations, 2015. 2, 3
- [25]25. Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kil- ian Q. Weinberger. Densely connected convolutional net- works. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. 6
- [29]29. Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen. Progressive Growing of GANs for Improved Quality, Stabil- ity, and Variation. In International Conference on Learning Representations, 2018. 1, 6, 8, 11
- [34]34. Yanpei Liu, Xinyun Chen, Shanghai Jiao Tong, Chang Liu, and Dawn Song. Delving into tranferable adversarial exam- ples and black-box attacks. In International Conference on Learning Representations, 2017. 3
- [41]41. Falko Matern, Christian Riess, and Mark Stamminger. Ex- ploiting visual artifacts to expose deepfakes and face ma- nipulations. In IEEE WACV Workshop on Image and Video Forensics, 2019. 1
- [58]58. Po-Wei Wu, Yu-Jing Lin, Che-Han Chang, Edward Y. Chang, and Shih-Wei Liao. Relgan: Multi-domain image- to-image translation via relative attributes. In IEEE Interna- tional Conference on Computer Vision (ICCV), 2019. 6, 8, 11
Cited by
Sorted by citations
Deepfakes and beyond: A Survey of face manipulation and fake detection
Tolosana, R., Vera-Rodriguez, R., Fierrez, J., Morales, A., & Ortega-Garcia, J. (2020) · Information Fusion · doi:10.1016/j.inffus.2020.06.014 · Cited by 1,237 · see citing works
Audio-Visual Person-of-Interest DeepFake Detection
Cozzolino, D., Pianese, A., Nießner, M., & Verdoliva, L. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00101 · Cited by 104 · see citing works
Self-citation
- Authors
- Cozzolino, D., Thies, J., Rossler, A., Niesner, M., & Verdoliva, L.
- Affiliations
- University of Naples Federico II (IT)
- Technical University of Munich (DE)
- Venue
- 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
- Publisher
- IEEE
- Published
- 2021-06
- Pages
- 990-1000
- OpenAlex
- W2990537444
- Cited by
- 54
- References
- 59
- Citations by year
- 2026: 2 · 2025: 6 · 2024: 6 · 2023: 20 · 2022: 13 · 2021: 2 · 2020: 4 · 2019: 1
- FWCI
- 3.35
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Air Force Research Laboratory
- Ministry of Education
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Steganography and Watermarking Techniques
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Keywords
- camera fingerprinting, synthetic media, image forgery detection, demosaicing, image compression artifacts, misinformation
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