Abstract
Image forgery detection has attracted widespread attention due to the enormous spread of forged images on the internet and social media. Many existing methods lack global contextual information and ignore the interaction between multi-level representations, which are not conducive to localizing the multi-scale tampered regions. To overcome these limitations, we present a novel method to detect splicing forgery of images by utilizing cross-scale interaction attention and multi-level global information. Firstly, we design a cross-scale interactive attention (CSIA) module for aggregating multi-level convolutional features, focusing selectively on task-relevant information. It allows representations learned at different levels to communicate effectively with each other. Secondly, by introducing the transformer layers with dynamic position embedding, the proposed method can capture the multi-level global contextual correlations of the image in a more flexible way. Extensive experiments have shown that our proposed method outperforms state-of-the-art methods. It can effectively detect, and segment multi-scale tampered regions by aggregating multi-scale local and global feature relevance.
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References
Carvalho, T., et al.: Illuminant-based transformed spaces for image forensics. IEEE Trans. Inf. Forensics Secur. 11(4), 720–733 (2015). https://doi.org/10.1109/TIFS.2015.2506548
Iakovidou, C., et al.: Content-aware detection of JPEG grid inconsistencies for intuitive image forensics. J. Vis. Commun. Image Represent. 54, 155–170 (2018). https://doi.org/10.1016/j.jvcir.2018.05.011
Qu, Z., Qiu, G., Huang, J.: Detect digital image splicing with visual cues. In: Katzenbeisser, S., Sadeghi, A.-R. (eds.) IH 2009. LNCS, vol. 5806, pp. 247–261. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-04431-1_18
Fang, Z., Wang, S., Zhang, X.: Image splicing detection using color edge inconsistency. In: 2010 International Conference on Multimedia Information Networking and Security (MINES), pp. 923–926 (2010). https://doi.org/10.1109/MINES.2010.196
Ferrara, P., Bianchi, T., De Rosa, A., Piva, A.: Image forgery localization via fine-grained analysis of CFA artifacts. IEEE Trans. Inf. Forensics Secur. 7(5), 1566–1577 (2012). https://doi.org/10.1109/TIFS.2012.2202227
Korus, P., Huang, J.: Multi-scale analysis strategies in PRNU-based tampering localization. IEEE Trans. Inf. Forensics Secur. 12(4), 809–824 (2016). https://doi.org/10.1109/TIFS.2016.2636089
Xiao, B., Wei, Y., Bi, X., Li, W., Ma, J.: Image splicing forgery detection combining coarse to refined convolutional neural network and adaptive clustering. Inf. Sci. 511, 172–191 (2020). https://doi.org/10.1016/j.ins.2019.09.038
Liu, B., Pun, C.M.: Exposing splicing forgery in realistic scenes using deep fusion network. Inf. Sci. 526, 133–150 (2020). https://doi.org/10.1016/j.ins.2020.03.099
Bappy, J.H., Simons, C., Nataraj, L., Manjunath, B.S., Roy-Chowdhury, A.K.: Hybrid LSTM and encoder-decoder architecture for detection of image forgeries. IEEE Trans. Image Process. 28(7), 3286–3300 (2019). https://doi.org/10.1109/TIP.2019.2895466
Wu, Y., AbdAlmageed, W., Natarajan, P.: ManTra-Net: manipulation tracing network for detection and localization of image forgeries with anomalous features. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9543–9552 (2019). https://doi.org/10.1109/CVPR.2019.00977
Ye, S., Sun, Q., Chang, E.C.: Detecting digital image forgeries by measuring inconsistencies of blocking artifact. In: 2007 IEEE International Conference on Multimedia and Expo (ICME), pp. 12–15 (2007). https://doi.org/10.1109/ICME.2007.4284574
Yan, Y., Ren, J., Sun, G., et al.: Unsupervised image saliency detection with Gestalt-laws guided optimization and visual attention-based refinement. Pattern Recogn. 79, 65–78 (2018). https://doi.org/10.1016/j.patcog.2018.02.004
Bi, X., Wei, Y., Xiao, B., & Li, W.: RRU-Net: the ringed residual U-Net for image splicing forgery detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 30–39 (2019). https://doi.org/10.1109/CVPRW.2019.00010
Bi, X., Zhang, Z., Xiao, B.: Reality transform adversarial generators for image splicing forgery detection and localization. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 14294–14303 (2021). https://doi.org/10.1109/ICCV48922.2021.01403
Ren, J., Wang, Z., Ren, J.: PS-Net: progressive selection network for salient object detection. Cogn. Comput. 14, 794–804 (2022). https://doi.org/10.1007/s12559-021-09952-4
Dong, J., Wang, W., Tan, T.: Casia image tampering detection evaluation database. In: 2013 IEEE China Summit and International Conference on Signal and Information Processing (ChinaSIP), pp. 422–426 (2013). https://doi.org/10.1109/ChinaSIP.2013.6625374
Hsu, Y.F., Chang, S.F.: Detecting image splicing using geometry invariants and camera characteristics consistency. In: 2006 IEEE International Conference on Multimedia and Expo (ICME), pp. 549–552 (2006). https://doi.org/10.1109/ICME.2006.262447
Guan, H., et al.: MFC datasets: large-scale benchmark datasets for media forensic challenge evaluation. In: 2019 IEEE Winter Applications of Computer Vision Workshops (WACVW), pp. 63–72 (2019). https://doi.org/10.1109/WACVW.2019.00018
Zampoglou, M., Papadopoulos, S., Kompatsiaris, Y.: Large-scale evaluation of splicing localization algorithms for web images. Multimed. Tools. Appl. 76(4), 4801–4834 (2017). https://doi.org/10.1007/s11042-016-3795-2
Oktay, O., Schlemper, J., Folgoc, L.: Attention U-net: learning where to look for the pancreas (2018). https://arxiv.org/abs/1804.03999v3
Mahfoudi, G., Tajini, B., Retraint, F., Morain-Nicolier, F., Pic, M.: Defacto: image and face manipulation dataset. In: 27th European Signal Processing Conference (EUSIPCO), pp. 1–5 (2019). https://doi.org/10.23919/EUSIPCO.2019.8903181
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Xu, Y., Zheng, J., Shao, C. (2024). Fusing Multi-scale Attention and Transformer for Detection and Localization of Image Splicing Forgery. In: Ren, J., et al. Advances in Brain Inspired Cognitive Systems. BICS 2023. Lecture Notes in Computer Science(), vol 14374. Springer, Singapore. https://doi.org/10.1007/978-981-97-1417-9_31
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DOI: https://doi.org/10.1007/978-981-97-1417-9_31
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