MiniNet: a concise CNN for image forgery detection

• Abadi M et al (2016) Tensorflow: a system for large-scale machine learning. 12th USENIX symposium on operating systems design and implementation (OSDI 16)

• Afchar D et al (2018) Mesonet: a compact facial video forgery detection network. In: 2018 IEEE International Workshop on Information Forensics and Security (WIFS). IEEE

• Agarap AF (2018) Deep learning using rectified linear units (relu). arXiv:1803.08375

• Ahmad M, Khursheed F (2021) Digital Image Forgery Detection Approaches: A Review. Applications of Artificial Intelligence in Engineering, Springer, Singapore

  Book  Google Scholar 

• Alexey D, Lucas B, Alexander K, Dirk W, Xiaohua Z, Thomas U, Mostafa D, Matthias M, Georg H, Sylvain G et al (2021) An image is worth 16x16 words: Transformers for image recognition at scale. In: International Conference on Learning Representations

• Ali SS et al (2022) Image forgery detection using deep learning by recompressing images. Electronics 11.3:403

• Bayar B, Matthew CS (2016) A deep learning approach to universal image manipulation detection using a new convolutional layer. In: Proceedings of the 4th ACM workshop on information hiding and multimedia security

• Bayar B, Stamm MC (2017) Design principles of convolutional neural networks for multimedia forensics. Electron Image 2017(7):77–86

  Article  Google Scholar 

• Bi X et al (2019) RRU-Net: the ringed residual U-Net for image splicing forgery detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops

• Bunk J et al (2017) Detection and localization of image forgeries using resampling features and deep learning. In: 2017 IEEE conference on computer vision and pattern recognition workshops (CVPRW). IEEE, 2017

• Chen H et al (2021) Hybrid features and semantic reinforcement network for image forgery detection. Multimed Syst pp 1–12

• Chen J, Kang X, Liu Y, Wang ZJ (2015) Median filtering forensics based on convolutional neural networks. IEEE Signal Process Lett 22(11):1849–1853

  Article  Google Scholar 

• Chollet F, et al. Keras. https://keras.io, 2015

• Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297

  Article  MATH  Google Scholar 

• Deng J et al (2009) Imagenet: a large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition. IEEE

• Dong J, Wang W, Tan T (2013) Casia image tampering detection evaluation database. In: 2013 IEEE China summit and international conference on signal and information processing. IEEE

• Ferrara P, Bianchi T, De Rosa A, Piva A (2012) Image forgery localization via fine-grained analysis of cfa artifacts. IEEE Trans Inf Forensics Secur 7(5):1566–1577

  Article  Google Scholar 

• Fridrich J, Kodovsky J (2012) Rich models for steganalysis of digital images. IEEE Trans Inf Forensics Secur 7(3):868–882

  Article  Google Scholar 

• Ghai A, Pradeep K, Samrat G (2021) A deep-learning-based image forgery detection framework for controlling the spread of misinformation. Information Technology and People

• Girshick R, Donahue J, Darrell T, Malik J (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 580–587)

• Gloe T, Rainer B (2010) The ’Dresden Image Database’ for benchmarking digital image forensics. In: Proceedings of the 2010 ACM Symposium on Applied Computing

• Gloe T, Böhme R (2010) The dresden image database for benchmarking digital image forensics. J Digit Forensic Pract 3(2–4):150–159

  Article  Google Scholar 

• Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2020) Generative adversarial networks. Commun ACM 63(11):139–44

  Article  MathSciNet  Google Scholar 

• Harris CR, Millman KJ, van der Walt SJ et al (2020) Array programming with NumPy. Nature 585:357–362. https://doi.org/10.1038/s41586-020-2649-2

  Article  Google Scholar 

• He K et al (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition

• Heckerman D, Wellman MP (1995) Bayesian networks. Commun ACM 38(3):27–31

  Article  Google Scholar 

• Hsu, Yu-Feng, and Shih-Fu Chang. “Detecting image splicing using geometry invariants and camera characteristics consistency.” 2006 IEEE International Conference on Multimedia and Expo. IEEE, 2006

• Huang G et al (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition

• Huh M et al (2018) Fighting fake news: Image splice detection via learned self-consistency.” Proceedings of the European Conference on Computer Vision (ECCV)

• Ioffe S, Szegedy C (2015) Batch normalization: Accelerating deep network training by reducing internal covariate shift. International conference on machine learning, PMLR

• Jiang, Jiaxi, Kai Zhang, and Radu Timofte. “Towards flexible blind JPEG artifacts removal.” Proceedings of the IEEE/CVF International Conference on Computer Vision. 2021

• Jiang, Liming, et al. “Deeperforensics-1.0: A large-scale dataset for real-world face forgery detection.” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020

• Kadam, Kalyani, Swati Ahirrao, and Ketan Kotecha. “AHP validated literature review of forgery type dependent passive image forgery detection with explainable AI.” International Journal of Electrical and Computer Engineering (2088-8708) 11.5 (2021)

• Katiyar, Ankit, and Arnav Bhavsar. “Image Forgery Detection with Interpretability.” arXiv preprint arXiv:2202.00908 (2022)

• Kharrazi M, Sencar H, Memon N (2005) Blind source camera identification. In: IEEE International Conference on Image Processing

• Kingma, Diederik P., and Jimmy Ba. “Adam: A method for stochastic optimization.” arXiv preprint arXiv:1412.6980 (2014)

• Koul, Saboor, et al. “An efficient approach for copy-move image forgery detection using convolution neural network.” Multimedia Tools and Applications (2022): 1-19

• Krishnaraj, N., et al. “Design of Automated Deep Learning-Based Fusion Model for Copy-Move Image Forgery Detection.” Computational Intelligence and Neuroscience 2022 (2022)

• Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 25:1097–1105

  Google Scholar 

• Kumar, Nitish, and Toshanlal Meenpal. “Salient keypoint-based copy-move image forgery detection.” Australian Journal of Forensic Sciences (2022): 1-24

• LeCun Y et al (1989) Backpropagation applied to handwritten zip code recognition. Neural Comput 1.4:541–551

• Lin, Zhiqiu, et al. “Visual chirality.” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020

• Mahdian B, Saic S (2009) Using noise inconsistencies for blind image forensics. Image Vis Comput 27(10):1497–1503

  Article  Google Scholar 

• McKinney, Wes. “Data structures for statistical computing in python.” Proceedings of the 9th Python in Science Conference. Vol. 445. No. 1. 2010

• Mingxing T, Quoc VL (2019) EfficientNet: Rethinking model scaling for convolutional neural networks. arXiv preprint arXiv:1905.11946

• Mingxing T, Quoc VL (2021) Efficientnetv2: smaller models and faster training. In: International conference on machine learning

• Nair V, Geoffrey EH (2010) Rectified linear units improve restricted boltzmann machines. ICML

• Neal Krawetz and Hacker Factor Solutions (2007) A picture’s worth. Hacker Factor. Solutions 6(2):2

• Rani A, Jain A (2022) Digital image forgery detection under complex lighting using Phong reflection model. J Electron Imaging 31(5):051402

  Article  Google Scholar 

• Rao Y, Ni J, Xie H (2021) Multi-semantic CRF-based attention model for image forgery detection and localization. Signal Process 183:108051

  Article  Google Scholar 

• Rössler, Andreas, et al. “Faceforensics: A large-scale video dataset for forgery detection in human faces.” arXiv preprint arXiv:1803.09179 (2018)

• Russakovsky O et al (2015) Imagenet large scale visual recognition challenge. Int J Comput Vis 115.3:211–252

• Simonyan, Karen, and Andrew Zisserman. “Very deep convolutional networks for large-scale image recognition.” arXiv preprint arXiv:1409.1556 (2014)

• Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1):1929–1958

  MathSciNet  MATH  Google Scholar 

• Stamm Matthew C, Min W, Ray LKJ (2013) Information forensics: an overview of the first decade. IEEE Access 1:167–200

  Article  Google Scholar 

• Szegedy C et al (2017) Inception-v4, inception-resnet and the impact of residual connections on learning. In: Thirty-first AAAI conference on artificial intelligence

• Szegedy, Christian, et al. “Going deeper with convolutions.” Proceedings of the IEEE conference on computer vision and pattern recognition. 2015

• Tyagi, Shobhit, and Divakar Yadav. “A Comprehensive Review on Image Synthesis with Adversarial Networks: Theory, Literature, and Applications.” Archives of Computational Methods in Engineering (2021): 1-21

• Tyagi, Shobhit, and Divakar Yadav. “A detailed analysis of image and video forgery detection techniques.” The Visual Computer (2022): 1-21

• Uijlings JR, Van De Sande KE, Gevers T, Smeulders AW (2013) Selective search for object recognition. Int J Comput Vision 104(2):154–171

  Article  Google Scholar 

• Wen B, Zhu Y, Subramanian R, Ng T, Shen X, Winkler S (2016) COVERAGE - A Novel Database for Copy-move Forgery Detection, in Proc. IEEE Int. Conf, Image Processing (ICIP)

  Google Scholar 

• Wu, Yue, Wael AbdAlmageed, and Premkumar Natarajan. “Mantra-net: Manipulation tracing network for detection and localization of image forgeries with anomalous features.” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2019

• Xuefeng Hu and Zhihan Zhang. Span: Spatial pyramid attention network for image manipulation localization. In ECCV, 2020

• Zhang, Ying, et al. “Image Region Forgery Detection: A Deep Learning Approach.” SG-CRC 2016 (2016): 1-11

• Zhou Peng , Xintong Han, Vlad I Morariu, and Larry S Davis. Learning rich features for image manipulation detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 1053-1061, 2018

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