Cited by 30Explainable Artificial Intelligence (XAI)Digital Media Forensic DetectionAdversarial Robustness in Machine Learning
The DOI matches its Crossref record; its reference list is not available.
An adversarial attack approach for eXplainable AI evaluation on deepfake detection models
Open accessOverview
Integrity signals
Registry
Crossref · 10.1016/j.cose.2023.103684
Cites
Statistics
Recomputing p-values needs the paper’s full text.
Verification
Claims the paper makes
We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Standard XAI evaluation frameworks may not be suitable for Deepfake detection, and the validity of explanations must themselves be scrutinized.
“As Gowrisankar et al. [31] emphasized, standard XAI evaluation frameworks may not be suitable for Deepfake detection, and the validity of explanations must themselves be scrutinized.”
From From Black Boxes to Glass Boxes: Explainable AI for Trustworthy Deepfake Forensics (2025) · cites this paper as [31]
The reliability of methods such as Grad-CAM has been questioned.
“The reliability of methods such as Grad-CAM has been questioned in this respect.”
From From Black Boxes to Glass Boxes: Explainable AI for Trustworthy Deepfake Forensics (2025) · cites this paper as [31]
Gowrisankar et al. extended this line of work by emphasizing faithfulness and robustness, introducing adversarial evaluation methods that examine whether explanations remain consistent under targeted perturbations.
“Gowrisankar et al. [31] extended this line of work by emphasizing faithfulness and robustness, introducing adversarial evaluation methods that examine whether explanations remain consistent under targeted perturbations.”
From From Black Boxes to Glass Boxes: Explainable AI for Trustworthy Deepfake Forensics (2025) · cites this paper as [31]
Gowrisankar et al. argued that generic evaluation methods such as random pixel removal are not well-suited for deepfake detectors and proposed using adversarial attack methods to evaluate explanations in a task-specific manner.
“Gowrisankar et al. [31] focused on the robustness of explanations. They argued that generic evaluation methods, such as random pixel removal, are not well-suited for Deepfake detectors. Instead, they proposed using adversarial attack methods to evaluate explanations in a more task-specific manner.”
From From Black Boxes to Glass Boxes: Explainable AI for Trustworthy Deepfake Forensics (2025) · cites this paper as [31]
Citations
No citation lists for this paper
No citation lists for this paper yet — checked again daily.
References 28 · Cited by 30
Research works printed but not linked: 30
- Afchar, D., Nozick, V., Yamagishi, J., Echizen I., 2018. MesoNet: a compact facial video forgery detection network, 2018 IEEE International Workshop On Information Forensics and Security (WIFS), Hong Kong, China, pp. 1–7, doi:10.1109/WIFS.2018.863076. · DOI · Paper page
- Bach, 2015, On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation, PLoS ONE, 10, 1, 10.1371/journal.pone.0130140 · DOI · Paper page
- Chollet, F., 2017. Xception: deep Learning with Depthwise Separable Convolutions, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, pp. 1800–1807, doi:10.1109/CVPR.2017.195. · DOI · Paper page
- Dai, 2023, Saliency attack: towards imperceptible black-box adversarial attack, ACM Trans. Intell. Syst. Technol., 14, 20, 10.1145/3582563 · DOI
- Dong, X., et al., 2020. Robust Superpixel-Guided Attentional Adversarial Attack, IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA, 2020, pp. 12892–12901, doi:10.1109/CVPR42600.2020.01291. · DOI
- Dong, S., Wang, J., Liang, J., Fan, H., Ji, R., (2022). Explaining Deepfake Detection by Analysing Image Matching. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13674. Springer, Cham. https://doi.org/10.1007/978-3-031-19781-9_2. · DOI
- Fel, T., Cadene, R., Chalvidal, M., Cord, M., Vigouroux, D., Serre, T., 2021. Look at the Variance! Efficient Black-box Explanations with Sobol-based Sensitivity Analysis, arXiv:abs/2111.04138.
- Gomez, T., Fréour, T., Mouchère, H., (2022). Metrics for Saliency Map Evaluation of Deep Learning Explanation Methods. In: El Yacoubi, M., Granger, E., Yuen, P.C., Pal, U., Vincent, N. (eds) Pattern Recognition and Artificial Intelligence. ICPRAI 2022. Lecture Notes in Computer Science, vol 13363. Springer, Cham. https://doi.org/10.1007/978-3-031-09037-0_8. · DOI
- Goodfellow, I.J., Shlens, J., Szegedy, C., 2015. Explaining and Harnessing Adversarial Examples, in 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, Conference Track Proceedings, Y. Bengio and Y. LeCun, Eds., 2015. [Online]. Available: http://arxiv.org/abs/1412.6572. · arXiv
- Gu, Z., Chen, Y., Yao, T., Ding, S., Li, J., Huang, F., Ma., L., 2021. Spatiotemporal Inconsistency Learning for DeepFake Video Detection. In Proceedings of the 29th ACM International Conference on Multimedia (MM '21). Association for Computing Machinery, New York, NY USA, 3473–3481. https://doi.org/10.1145/3474085.3475508. · DOI · Paper page
- Hooker, 2019, A benchmark for interpretability methods in deep neural networks, Adv. Neural Inf. Process. Syst., 32, 9737
- Hussain, S., Neekhara, P., Jere, M., Koushanfar F., McAuley, J., 2021. Adversarial Deepfakes: evaluating Vulnerability of Deepfake Detectors to Adversarial Examples, 2021 IEEE Winter Conference on Applications of Computer Vision (WACV), Waikoloa, HI, USA, pp. 3347–3356, doi:10.1109/WACV48630.2021.00339. · DOI · Paper page
- Ismail, 2021, Deepfake video detection: yOLO-face convolution recurrent approach, PeerJ Comput. Sci., 7, e730, 10.7717/peerj-cs.730 · DOI
- Kapishnikov, A., Bolukbasi, T., Viegas F., Terry, M., 2019. XRAI: better Attributions Through Regions, 2019 IEEE/CVF International Conference on Computer Vision (ICCV), Seoul, Korea (South), pp. 4947–4956, doi:10.1109/ICCV.2019.00505. · DOI
- Kurakin, A., Goodfellow, I.J., Bengio, S., 2017. Adversarial Machine Learning at Scale, in 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, Conference Track Proceedings. OpenReview.net, 2017. [Online]. Available: https://openreview.net/forum?id=BJm4T4Kgx.
- Lin, 2019, Do explanations reflect decisions? A machinecentric strategy to quantify the performance of explainability algorithms, CoRR
- Lundberg S.M., Lee, S.I., 2017. A Unified Approach to Interpreting Model Predictions, in Advances in Neural Information Processing Systems 30, I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan et al., Eds. Curran Associates, Inc., pp. 4765–4774. [Online]. Available: http://papers.nips.cc/paper/7062-aunified-approach-to-interpreting-model-predictions.pdf.
- Neekhara, P., Dolhansky, B., Bitton, J., Ferrer, C., 2021. Adversarial threats to deepfake detection: a practical perspective, in 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). Los Alamitos, CA, USA: IEEE Computer Society, pp. 923–932. [Online]. Available: https://doi.ieeecomputersociety.org/10.1109/CVPRW53098.2021.0010. · DOI · Paper page
- Petsiuk, V., Das, A., Saenko, K., 2018. RISE: randomized Input Sampling for Explanation of Black-box Models, arXiv:abs/1806.07421.
- Rössler, A., Cozzolino, D., Verdoliva, L., Riess, C., Thies J., Niessner, M., 2019. FaceForensics++: learning to Detect Manipulated Facial Images, 2019 IEEE/CVF International Conference on Computer Vision (ICCV), Seoul, Korea (South), pp. 1–11, doi:10.1109/ICCV.2019.00009. · DOI · Paper page
- Ribeiro, M.T., Singh, S., Guestrin, C., 2016. Why Should I Trust You?: explaining the Predictions of Any Classifier. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '16). Association for Computing Machinery, New York, NY, USA, 1135–1144. https://doi.org/10.1145/2939672.2939778. · DOI · Paper page
- Rieger L., Hansen, L.K., 2020. IROF: a low resource evaluation metric for explanation methods [Online]. Available: https://arxiv.org/abs/2003.08747. · arXiv
- Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh D., Batra, D., 2017. Grad-CAM: visual explanations from deep networks via gradient-based localization, 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy, pp. 618–626, doi:10.1109/ICCV.2017.74. · DOI · Paper page
- Storn, 1997, Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces, J. Glob. Optim., 11, 341, 10.1023/A:1008202821328 · DOI · Paper page
- Sundararajan, M., Taly, A., Yan, Q., 2017. Axiomatic Attribution for Deep Networks, in Proceedings of the 34th International Conference on Machine Learning - Volume 70, ser. ICML’17. JMLR.org, p. 3319–3328.
- Wierstra, 2014, Natural evolution strategies, J. Machine Learn. Res., 15, 949
- Xiang, T., Liu, H., Guo, S., Zhang, T., & Liao, X., 2021. Local Black-box Adversarial Attacks: a Query Efficient Approach, arXiv:abs/2101.01032.
- Yeh, C.-K., Hsieh, C.-Y., Suggala, A.S., Inouye, D.I., Ravikumar. P., 2019. On the (in)fidelity and sensitivity of explanations. Proceedings of the 33rd International Conference on Neural Information Processing Systems. Curran Associates Inc., Red Hook, NY, USA, Article 984, 10967–10978.
- Zhang, 2018, Topdown neural attention by excitation backprop, Int. J. Comput. Vis., 126, 1084, 10.1007/s11263-017-1059-x · DOI
- Zhou, B., Khosla, A., Lapedriza, A., Oliva A., Torralba, A., 2016. Learning Deep Features for Discriminative Localization, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, pp. 2921–2929, doi:10.1109/CVPR.2016.319. · DOI · Paper page
Metadata
- Authors
- Gowrisankar, B., & Thing, V. L. L.
- Affiliations
- ST Engineering (Singapore) (SG)
- Corresponding author
- Gowrisankar, Balachandar
- Venue
- Computers & Security
- Publisher
- Elsevier BV
- Published
- 2024-04
- Volume
- 139
- Pages
- 103684
- ISSN
- 0167-4048 (ISSN-L)
- OpenAlex
- W4390116384
- Cited by
- 30
- References
- 28
- Citations by year
- 2026: 11 · 2025: 15 · 2024: 4
- FWCI
- 2.83
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- Computers & Security (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by-nc-nd)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Keywords
- deepfake detection, adversarial attacks, saliency maps, model interpretability, image classification
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
