Abstract-In order to verify the authenticity of digital images, researchers have begun developing digital forensic techniques to identify image editing. One editing operation that has recently received increased attention is median filtering. While several median filtering detection techniques have recently been developed, their performance is degraded by JPEG compression. These techniques suffer similar degradations in performance when a small window of the image is analyzed, as is done in localized filtering or cut-and-paste detection, rather than the image as a whole. In this paper, we propose a new, robust median filtering forensic technique. It operates by analyzing the statistical properties of the median filter residual (MFR), which we define as the difference between an image in question and a median filtered version of itself. To capture the statistical properties of the MFR, we fit it to an autoregressive (AR) model. We then use the AR coefficients as features for median filter detection. We test the effectiveness of our proposed median filter detection techniques through a series of experiments. These results show that our proposed forensic technique can achieve important performance gains over existing methods, particularly at low false-positive rates, with a very small dimension of features.Index Terms-Median filtering, noise residual, image forensics, autoregressive model.
Sensor pattern noise (SPN) has been recognized as a reliable device fingerprint for camera source identification (CSI) and image origin verification. However, the SPN extracted from a single image can be contaminated largely by image content details from scene because, for example, an image edge can be much stronger than SPN and hard to be separated. So, the identification performance is heavily dependent upon the purity of the estimated SPN. In this paper, we propose an effective SPN predictor based on eight-neighbor context-adaptive interpolation algorithm to suppress the effect of image scene and propose a source camera identification method with it to enhance the receiver operating characteristic (ROC) performance of CSI. Experimental results on different image databases and on different sizes of images show that our proposed method has the best ROC performance among all of the existing CSI schemes, as well as the best performance in resisting mild JPEG compression, especially when the false-positive rate is held low. Because trustworthy CSI must often be performed at low false-positive rates, these results demonstrate that our proposed technique is better suited for use in real-world scenarios than existing techniques. However, our proposed method needs many such as not less than 100 original images to create camera fingerprint; the advantage of the proposed method decreases when the camera fingerprint is created with less original images.
Compression of encrypted data draws much attention in recent years due to the security concerns in a service-oriented environment such as cloud computing. We propose a scalable lossy compression scheme for images having their pixel value encrypted with a standard stream cipher. The encrypted data are simply compressed by transmitting a uniformly subsampled portion of the encrypted data and some bitplanes of another uniformly subsampled portion of the encrypted data. At the receiver side, a decoder performs content-adaptive interpolation based on the decrypted partial information, where the received bit plane information serves as the side information that reflects the image edge information, making the image reconstruction more precise. When more bit planes are transmitted, higher quality of the decompressed image can be achieved. The experimental results show that our proposed scheme achieves much better performance than the existing lossy compression scheme for pixel-value encrypted images and also similar performance as the state-of-the-art lossy compression for pixel permutation-based encrypted images. In addition, our proposed scheme has the following advantages: at the decoder side, no computationally intensive iteration and no additional public orthogonal matrix are needed. It works well for both smooth and texture-rich images.
Current machine vision-based detection methods for metal surface roughness mainly use the grey values of images for statistical analysis but do not make full use of the colour information and ignore the subjective judgment of the human vision system. To address these problems, this paper proposes a method to measure surface roughness through the sharpness evaluation of colour images. Based on the difference in sharpness of virtual images of colour blocks that are formed on grinding surfaces with different roughness, an algorithm for evaluating the sharpness of colour images that is based on the difference of the RGB colour space was used to develop a correlation model between the sharpness and the surface roughness. The correlation model was analysed under two conditions: constant illumination and varying illumination. The effect of the surface textures of the grinding samples on the image sharpness was also considered, demonstrating the feasibility of the detection method. The results show that the sharpness is strongly correlated with the surface roughness; when the illumination and the surface texture have the same orientation, the sharpness clearly decreases with increasing surface roughness. Under varying illumination, this correlation between the sharpness and surface roughness was highly robust, and the sharpness of each virtual image increased linearly with the illumination. Relative to the detection method for surface roughness using gray level co-occurrence matrix or artificial neural network, the proposed method is convenient, highly accurate and has a wide measurement range.
This paper addresses the median filtering forensics for a lossy compressed image with low resolution, which is essential for the identification of fake images and fake videos. A deep residual model with training data augmentation is employed in the proposed method. To solve the dilemma that the low-resolution image is the lack of enough statistical pixels for extracting reliable features, we propose a filter layer to widen the inputs for the convolutional neural network (CNN). First, we perform the high-pass filtering to an image in the filtered layer and stack the multiple filtered residuals into 16-channel feature maps as inputs of CNN. Then, a deep residual CNN model has proposed to self-learn the median filtering traces that are hidden in the JPEG lossy compressed image. To alleviate the over-fitting issue of the deeper CNN model, we employ a data augmentation scheme in the training to increase the diversity of training data and, thus, obtain a more stable median filtering detector. The experimental results demonstrate that the proposed net with training data augmentation outperforms state of the arts in both baseline test and generalization ability test, achieving at least 2% higher in terms of detection accuracy.INDEX TERMS Multimedia security, median filtering forensics, deep learning, convolutional neural network.
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