Distinguishing computer graphics from natural images using convolution neural networks

By Nicolas Rahmouni, Vincent Nozick, Junichi Yamagishi, Isao Echizen. International Workshop on Information Forensics and Security, 2017

309 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/ccd354af0b0f54cd202727edd1280f09f061d7f8

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

Citations in the corpus

Deep learning for deepfakes creation and detection: A survey (2019, methodology): “Videos/ Images Four datasets: the Idiap Research Institute replayattack [126], deepfake face swapping by [102], facial reenactment FaceForensics [127], and fully computergenerated image set using [128].”
Media Forensics and DeepFakes: An Overview (2020, methodology): “Some methods [145], [146] train the CNN to extract compact features at”
Two-branch Recurrent Network for Isolating Deepfakes in Videos (2020, unknown): “84 [49] Rahmouni et al.”
MesoNet: a Compact Facial Video Forgery Detection Network (2018, unknown): “[17] distinguish computer graphics from photographic images.”
Detection of GAN-Generated Fake Images over Social Networks (2018, methodology): “Some are methods proposed for the detection of generic image manipulations [3], [24], [25] or specifically designed for computer graphics image detection [13].”
DeepFakes and Beyond: A Survey of Face Manipulation and Fake Detection (2020, methodology): “…content of the image [72], iii) a CNN-based system with a global pooling layer that computes four statistics (mean, variance, maximum, and minimum) [73], iv) the CNN MesoInception-4 detection system described in [53], and ﬁnally v) the CNN-based system XceptionNet [74] pre-trained using ImageNet…”
Face X-Ray for More General Face Forgery Detection (2019, background): “With the tremendous success of deep learning, some works [10, 33, 7] adopt neural networks to automatically extract discriminative features for forgery detection.”
Deepfake Video Detection Using Recurrent Neural Networks (2018, unknown): “[33].”
Exploiting Visual Artifacts to Expose Deepfakes and Face Manipulations (2019, background): “Other recent methods aim to distinguish computer generated from natural images [31, 5].”
Capsule-forensics: Using Capsule Networks to Detect Forged Images and Videos (2018, methodology): “Finally, we compared the performance of our proposed method with that of state-of-the-art methods on computer-generated images (CGIs) and photographic images (PIs) on the dataset proposed by Rahmouni et al. [25].”
DeepFake Detection by Analyzing Convolutional Traces (2020, background): “They addressed the problem of fake detection as a binary classification problem for each frame of manipulated videos, considering different techniques present in the state of the art [1, 4, 7, 8, 10, 34].”
Multi-task Learning for Detecting and Segmenting Manipulated Facial Images and Videos (2019, methodology): “The sliding window approach is used more for detecting spoofing regions generated by a computer to create spoof images or videos from bona fide ones [25, 21, 26].”
Deep Learning for Deepfakes Creation and Detection (2019, methodology): “Four data sets: the Idiap Research Institute replay-attack [88], deepfake face swapping by [73], facial reenactment FaceForensics [89], and fully computer-generated image set using [91].”
Hybrid LSTM and Encoder–Decoder Architecture for Detection of Image Forgeries (2019, background): “Recently, there has been a growing interest to detect image manipulations by applying different computer vision and machine learning algorithms [70], [76].”
Thinking in Frequency: Face Forgery Detection by Mining Frequency-aware Clues (2020, unknown): “74% CustomPooling CNN [49] 61.”
Detecting GAN generated Fake Images using Co-occurrence Matrices (2019, background): “Related ﬁelds also include detection of computer generated (CG) images [46, 47, 48, 49].”
Detecting CNN-Generated Facial Images in Real-World Scenarios (2020, methodology): “Following these works, learning-based methods have been proposed, using CNNs to automatically learn features of real and generated images [1, 3, 7, 12, 13, 40].”
Frequency-aware Discriminative Feature Learning Supervised by Single-Center Loss for Face Forgery Detection (2021, background): “Early research is keen on utilizing hand-crafted features or modifying the structure of existing neural networks[46, 5, 4, 34].”