Splicebuster: A new blind image splicing detector

By Davide Cozzolino, Giovanni Poggi, Luisa Verdoliva. 2015

267 citations recorded by the source corpus.

Subfields: 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].”
Recasting Residual-based Local Descriptors as Convolutional Neural Networks: an Application to Image Forgery Detection (2017, background): “Such subtle traces, hardly visible without enhancement, may reveal anomalies due to object insertion [8, 9] or allow detecting dierent types of image editing operations [4, 20, 26].”
Image Splicing Localization using a Multi-task Fully Convolutional Network (MFCN) (2017, background): “The ﬁrst class of splicing localization algorithms exploits noise patterns under the assumption that diﬀerent images have diﬀerent noise patterns as a result of a combination of diﬀerent camera makes/models, the capture parameters of each image, and post-processing techniques [26, 27, 9, 7, 8, 21].”
Media Forensics and DeepFakes: An Overview (2020, methodology): “Therefore, in [63] the high-pass noise residual of the image is used to extract rich features which better characterize”
Autoencoder with recurrent neural networks for video forgery detection (2017, methodology): “The method proposed here takes inspiration from our previous work on the detection and localization of splicing in still images [10, 11].”
Photo Forensics From Rounding Artifacts (2020, methodology): “…forensic algorithms: two are based on JPEG blocking artifacts (BAG [28] and CAGI [20]), three are based on AC coeicient distributions (CDA [29], I-CDA [7], and BG-CDA [6]), one is based on irst digit distribution of AC coeicients (FDF-A [2]), and one is based on noise residuals (SB [11]).”
ManTra-Net: Manipulation Tracing Network for Detection and Localization of Image Forgeries With Anomalous Features (2019, methodology): “Furthermore, methods that provide localization capabilities often rely on heavy, time-consuming pre-and/or post-processing, e.g ., patch extraction [53], expectation-maximization [19, 20], feature clustering [14, 11, 32, 28], segmentation [32, 28, 15], etc .”
Exploiting Visual Artifacts to Expose Deepfakes and Face Manipulations (2019, methodology): “Recent statistical methods form statistical fingerprints on the residuals of an image to detect manipulations [11, 6, 7], validate noise priors from metadata [14], or learn specific manipulation traces, such as recoloring [3] or recompression [28].”
Noiseprint: A CNN-Based Camera Model Fingerprint (2018, methodology): “The interested reader is referred to [26] for a more detailed description.”
Learning Rich Features for Image Manipulation Detection (2018, background): “[7] explore and demonstrate the performance of SRM features in distinguishing tampered and authentic regions.”
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].”
A deep learning approach to detection of splicing and copy-move forgeries in images (2016, methodology): “forgery detection schemes [20][21] by incorporating the highpass filters devised in SRM.”
Two-Stream Neural Networks for Tampered Face Detection (2017, methodology): “This is inspired by recent research on CNNs showing the potential to learn tampering evidence [3, 21, 7] and rich models [15, 9, 10, 16], which are models of the noise components that produce informative features for ste-ganalysis and we call the features produced by these mod-1”
DeepFakes and Beyond: A Survey of Face Manipulation and Fake Detection (2020, background): “move different elements of the image [32], [33], reduce the frame rate in a video [34]–[36], etc.”
SPAN: Spatial Pyramid Attention Network forImage Manipulation Localization (2020, methodology): “has been work on both detection and localization of manipulations in recent years. Among the methods that include localization, many deal with only one or a few types of manipulations such as splicing[8,16,18], copy-move[7,27,33,35,36], removal[44], and enhancement[5,6]. Some recent papers have prop”
PSCC-Net: Progressive Spatio-Channel Correlation Network for Image Manipulation Detection and Localization (2021, background): “, splicing [2, 5, 10, 11, 23, 29, 41, 62], copy-move [9, 24, 59, 63, 64], removal [71], and the content-preserved process [4, 27].”