Hybrid LSTM and Encoder-Decoder Architecture for Detection of Image Forgeries.

With advanced image journaling tools, one can easily alter the semantic meaning of an image by exploiting certain manipulation techniques such as copy clone, object splicing, and removal, which mislead the viewers. In contrast, the identification of these manipulations becomes a very challenging task as manipulated regions are not visually apparent. This paper proposes a high-confidence manipulation localization architecture that utilizes resampling features, long short-term memory (LSTM) cells, and an encoder-decoder network to segment out manipulated regions from non-manipulated ones. Resampling features are used to capture artifacts, such as JPEG quality loss, upsampling, downsampling, rotation, and shearing. The proposed network exploits larger receptive fields (spatial maps) and frequency-domain correlation to analyze the discriminative characteristics between the manipulated and non-manipulated regions by incorporating the encoder and LSTM network. Finally, the decoder network learns the mapping from low-resolution feature maps to pixel-wise predictions for image tamper localization. With the predicted mask provided by the final layer (softmax) of the proposed architecture, end-to-end training is performed to learn the network parameters through back-propagation using the ground-truth masks. Furthermore, a large image splicing dataset is introduced to guide the training process. The proposed method is capable of localizing image manipulations at the pixel level with high precision, which is demonstrated through rigorous experimentation on three diverse datasets

Detection and Localization of Image Forgeries using Resampling Features and Deep Learning

Resampling is an important signature of manipulated images. In this paper, we propose two methods to detect and localize image manipulations based on a combination of resampling features and deep learning. In the first method, the Radon transform of resampling features are computed on overlapping image patches. Deep learning classifiers and a Gaussian conditional random field model are then used to create a heatmap. Tampered regions are located using a Random Walker segmentation method. In the second method, resampling features computed on overlapping image patches are passed through a Long short-term memory (LSTM) based network for classification and localization. We compare the performance of detection/localization of both these methods. Our experimental results show that both techniques are effective in detecting and localizing digital image forgeries

Detection and Localization of Image Forgeries using Resampling Features and Deep Learning

Resampling is an important signature of manipulated images. In this paper, we propose two methods to detect and localize image manipulations based on a combination of resampling features and deep learning. In the first method, the Radon transform of resampling features are computed on overlapping image patches. Deep learning classifiers and a Gaussian conditional random field model are then used to create a heatmap. Tampered regions are located using a Random Walker segmentation method. In the second method, resampling features computed on overlapping image patches are passed through a Long short-term memory (LSTM) based network for classification and localization. We compare the performance of detection/localization of both these methods. Our experimental results show that both techniques are effective in detecting and localizing digital image forgeries