Median Filtering Forensics Based on Convolutional Neural Networks

By Jiansheng Chen, Xiangui Kang, Ye Liu, Z. Jane Wang. IEEE Signal Processing Letters, 2015

394 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/05d408a8b720f3b45a7e3a83fb14015554133582

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In Computer Vision and Pattern Recognition

Citations in the corpus

ManTra-Net: Manipulation Tracing Network for Detection and Localization of Image Forgeries With Anomalous Features (2019, neutral): “[16] Pixel residual DNN – –”
A deep learning approach to detection of splicing and copy-move forgeries in images (2016, methodology): “In [14], a CNN model was trained for median filtering detection.”
Two-Stream Neural Networks for Tampered Face Detection (2017, methodology): “Recently, some methods based on Convolutional Neural Networks (CNN) [3, 7, 21] have achieved very good results.”
Hybrid LSTM and Encoder–Decoder Architecture for Detection of Image Forgeries (2019, methodology): “Some recent deep learning based methods such as stacked auto-encoders (SAE) [88] and convolutional neural networks (CNN) [8], [21], [77] have also been applied to detect/classify image manipulations.”
Detecting CNN-Generated Facial Images in Real-World Scenarios (2020, methodology): “Image pre-processing Pre-processing an image before passing it to a CNN-based model is not uncommon in the field of image forgery detection and has been studied by several works [5, 11, 12, 13, 24, 25, 33, 41].”
Detecting Photoshopped Faces by Scripting Photoshop (2019, methodology): “[11] use a convolutional network to detect median filtering.”
Constrained Convolutional Neural Networks: A New Approach Towards General Purpose Image Manipulation Detection (2018, background): “Examples of these include resampling detectors that use the variance of prediction residues [3], [32], median filter streaking artifact residuals [7], median filter residual features [8], [33], SPAM features [21], and rich model predictors [19].”
Learning Rich Features for Image Manipulation Detection (2018, methodology): “[5] add a low pass filter layer before a CNN to detect median filtering tampering techniques.”
A Deep Learning Approach to Universal Image Manipulation Detection Using a New Convolutional Layer (2016, methodology): “during their efforts train a CNN to perform median filtering detection [2].”