SRU-NET: SOBEL RESIDUAL U-NET FOR IMAGE MANIPULATION DETECTION

Recently, most successful image manipulation detection methods have been based on convolutional neural networks (CNNs). Nevertheless, Existing CNN methods have limited abilities. CNN-based detection networks tend to extract signal features strongly related to content. However, image manipulation detection tends to extract weak signal features that are weakly related to content. To address this issue, We propose a novel Sobel residual neural network with adaptive central difference convolution, an extension of the classical U-Net architecture, for image manipulation detection. Adaptive central differential convolution can capture the essential attributes of an image by gathering intensity and gradient information. Sobel residual gradient block can capture forgery edge discriminative details. Extensive experimental results show that our method can significantly improve the accuracy of localising the forged region compared with the state-of-the-art methods

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 which utilizes resampling features, Long-Short Term Memory (LSTM) cells, and encoder-decoder network to segment out manipulated regions from non-manipulated ones. Resampling features are used to capture artifacts like 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 manipulated and non-manipulated regions by incorporating encoder and LSTM network. Finally, decoder network learns the mapping from low-resolution feature maps to pixel-wise predictions for image tamper localization. With predicted mask provided by final layer (softmax) of the proposed architecture, end-to-end training is performed to learn the network parameters through back-propagation using 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 pixel level with high precision, which is demonstrated through rigorous experimentation on three diverse datasets

Recasting Residual-based Local Descriptors as Convolutional Neural Networks: an Application to Image Forgery Detection

Local descriptors based on the image noise residual have proven extremely effective for a number of forensic applications, like forgery detection and localization. Nonetheless, motivated by promising results in computer vision, the focus of the research community is now shifting on deep learning. In this paper we show that a class of residual-based descriptors can be actually regarded as a simple constrained convolutional neural network (CNN). Then, by relaxing the constraints, and fine-tuning the net on a relatively small training set, we obtain a significant performance improvement with respect to the conventional detector

Localization of JPEG double compression through multi-domain convolutional neural networks

When an attacker wants to falsify an image, in most of cases she/he will perform a JPEG recompression. Different techniques have been developed based on diverse theoretical assumptions but very effective solutions have not been developed yet. Recently, machine learning based approaches have been started to appear in the field of image forensics to solve diverse tasks such as acquisition source identification and forgery detection. In this last case, the aim ahead would be to get a trained neural network able, given a to-be-checked image, to reliably localize the forged areas. With this in mind, our paper proposes a step forward in this direction by analyzing how a single or double JPEG compression can be revealed and localized using convolutional neural networks (CNNs). Different kinds of input to the CNN have been taken into consideration, and various experiments have been carried out trying also to evidence potential issues to be further investigated.Comment: Accepted to CVPRW 2017, Workshop on Media Forensic

Aligned and Non-Aligned Double JPEG Detection Using Convolutional Neural Networks

Due to the wide diffusion of JPEG coding standard, the image forensic community has devoted significant attention to the development of double JPEG (DJPEG) compression detectors through the years. The ability of detecting whether an image has been compressed twice provides paramount information toward image authenticity assessment. Given the trend recently gained by convolutional neural networks (CNN) in many computer vision tasks, in this paper we propose to use CNNs for aligned and non-aligned double JPEG compression detection. In particular, we explore the capability of CNNs to capture DJPEG artifacts directly from images. Results show that the proposed CNN-based detectors achieve good performance even with small size images (i.e., 64x64), outperforming state-of-the-art solutions, especially in the non-aligned case. Besides, good results are also achieved in the commonly-recognized challenging case in which the first quality factor is larger than the second one.Comment: Submitted to Journal of Visual Communication and Image Representation (first submission: March 20, 2017; second submission: August 2, 2017