Identifying Human Edited Images using a CNN
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Abstract (arXiv)
Most non-professional photo manipulations are not made using propriety software like Adobe Photoshop, which is expensive and complicated to use for the average consumer selfie-taker or meme-maker. Instead, these individuals opt for user friendly mobile applications like FaceTune and Pixlr to make human face edits and alterations. Unfortunately, there is no existing dataset to train a model to classify these type of manipulations. In this paper, we present a generative model that approximates the distribution of human face edits and a method for detecting Facetune and Pixlr manipulations to human faces.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 8 of its 25 references that are in the map. First posted on arXiv 9 Jan 2021.
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References · 8
- 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 6,013
- 2016Aggregated Residual Transformations for Deep Neural Networkscited by 2,546
- 2015Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networkscited by 2,054
- 2017Dilated Residual Networkscited by 294
- 2017AttGAN: Facial Attribute Editing by Only Changing What You Wantcited by 242
- 2018Two-Stream Neural Networks for Tampered Face Detectioncited by 125
- 2019Detecting Photoshopped Faces by Scripting Photoshopcited by 55
- 2019Collapse Resistant Deep Convolutional GAN for Multi-Object Image Generationcited by 2
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