A deep learning approach to detection of splicing and copy-move forgeries in images

By Yuan Rao, Jiangqun Ni. 2016

485 citations recorded by the source corpus.

Subfields: Biophysics, Computer Vision and Pattern Recognition, Media Technology

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Citations in the corpus

CNN-Generated Images Are Surprisingly Easy to Spot… for Now (2019, background): “More recently, researchers have applied learning-based methods to these problems [51, 18, 13, 38, 44].”
Image Splicing Localization using a Multi-task Fully Convolutional Network (MFCN) (2017, background): “Of the existing techniques in the published literature on image forensics, relatively few have utilized deep learning or neural-network-based techniques [29, 38, 3, 10].”
Towards Universal Fake Image Detectors that Generalize Across Generative Models (2023, methodology): “Learning based methods have been used to detect manipulated images as well [15, 44, 49].”
ForensicTransfer: Weakly-supervised Domain Adaptation for Forgery Detection (2018, background): “This can be accomplished by adding a first layer of fixed high-pass filters [33, 27], learned filters [7], or even by recasting hand-crafted features working on residuals as a convolutional neural network (CNN) [11].”
SPAN: Spatial Pyramid Attention Network forImage Manipulation Localization (2020, methodology): “cy compared to state-of-the-art methods. 2 Related Work Manipulation Detection and Localization: Most of existing methods detect and localization a specic type of manipulation only, such as copy-move[7,27,33], removal[44], enhancement[5,6], and splicing[8,16,18,29]. Among these types, splice detecti”
Detecting CNN-Generated Facial Images in Real-World Scenarios (2020, methodology): “Several works on image forgery detection [12, 13, 25, 32, 41] include high-pass filters as a way to accentuate the high-frequency structure of an image.”
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.”
Detecting GAN generated Fake Images using Co-occurrence Matrices (2019, background): “Recent approaches have focused on applying deep learning based methods to detect tampered images [30, 31, 32, 33, 34, 9, 35] The detection of GAN images is a new area in image forensics and there are very few papers in this area [36, 37, 38, 39, 40, 41, 42, 43, 44, 45].”
Noiseprint: A CNN-Based Camera Model Fingerprint (2018, background): “layer of high-pass filters, either fixed [46], [47], or trainable [48], [49], or else by recasting a conventional feature extractor”
ManTra-Net: Manipulation Tracing Network for Detection and Localization of Image Forgeries With Anomalous Features (2019, background): “Most of the existing methods [9, 25, 37, 38, 49] only focus on image-level detection— whether or not an image is forged.”
Hybrid LSTM and Encoder–Decoder Architecture for Detection of Image Forgeries (2019, methodology): “Recent efforts, including [8], [9], [17], [62], [77] in the manipulation detection task, exploit deep learning based models.”
Learning Rich Features for Image Manipulation Detection (2018, methodology): “[27] use an SRM filter kernel as initialization for a CNN to boost the detection accuracy.”
Media Forensics and DeepFakes: An Overview (2020, methodology): “In [135], the fixed rich-model filters are adopted, and the network is used only for feature extraction, followed by a SVM in charge of making the global decision.”
MesoNet: a Compact Facial Video Forgery Detection Network (2018, background): “Rao and Ni [18] propose a network to detect image splicing.”