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Found 9 papers, 3 papers with code
Pose Guided Fashion Image Synthesis Using Deep Generative Model
In order to formulate the framework, we employ one generator and two discriminators for image synthesis.
A Skip Connection Architecture for Localization of Image Manipulations
In order to exploit these traces in localizing the tampered regions, we propose an encoder-decoder based network where we fuse representations from early layers in the encoder (which are richer in low-level spatial cues, like edges) by skip pooling with representations of the last layer of the decoder and use for manipulation detection.
Detecting GAN generated Fake Images using Co-occurrence Matrices
The advent of Generative Adversarial Networks (GANs) has brought about completely novel ways of transforming and manipulating pixels in digital images.
Hybrid LSTM and Encoder-Decoder Architecture for Detection of Image Forgeries
This paper proposes a high-confidence manipulation localization architecture which utilizes resampling features, Long-Short Term Memory (LSTM) cells, and encoder-decoder network to segment out manipulated regions from non-manipulated ones.
Boosting Image Forgery Detection using Resampling Features and Copy-move analysis
Realistic image forgeries involve a combination of splicing, resampling, cloning, region removal and other methods.
Exploiting Spatial Structure for Localizing Manipulated Image Regions
In order to formulate the framework, we employ a hybrid CNN-LSTM model to capture discriminative features between manipulated and non-manipulated regions.
Detection and Localization of Image Forgeries using Resampling Features and Deep Learning
In this paper, we propose two methods to detect and localize image manipulations based on a combination of resampling features and deep learning.
The Impact of Typicality for Informative Representative Selection
In computer vision, selection of the most informative samples from a huge pool of training data in order to learn a good recognition model is an active research problem.
Non-Uniform Subset Selection for Active Learning in Structured Data
We construct a graph from the unlabeled data to represent the underlying structure, such that each node represents a data point, and edges represent the inter-relationships between them.
