Anti-forensic Analysis for Image Splicing Detection Through Advanced Filters
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-9524-0_14
Reference22 articles.
1. Abd El-Latif EI, Taha A, Zayed HH (2020) A passive approach for detecting image splicing based on deep learning and wavelet transform. Arab J Sci Eng 45:3379–3386
2. Alamro L, Yusoff N (2017) Copy–move forgery detection using integrated dwt and surf. J Telecommun Electron Comput Eng (JTEC) 9(1–2):67–71
3. Bappy JH, Simons C, Nataraj L, Manjunath BS, Roy-Chowdhury AK (2019) Hybrid LSTM and encoder–decoder architecture for detection of image forgeries. IEEE Trans Image Process 28(7):3286–3300. https://doi.org/10.1109/TIP.2019.2895466
4. Böhme R, Kirchner M (2012) Counter-forensics: attacking image forensics. In: Digital image forensics: there is more to a picture than meets the eye. Springer, pp 327–366
5. Das D, Bhunia B, Naskar R, Chakraborty RS (2022) Linear and non-linear filter-based counter-forensics against image splicing detection. In: International conference on computer vision and image processing. Springer, pp 261–275
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