Recent advances in machine learning and computer vision have made it simple to manipulate a variety of media, including satellite images. Most of the commercially available satellite images go through the process of orthorectification to remove potential distortions due to terrain variations. This orthorectification process typically involves the use of rational polynomial coefficients (RPC) that geometrically remap the pixels in the original image to the rectified image. This paper proposes the first method to verify the authenticity of RPC metadata in an orthorectified satellite image. The steps include calculating the Residual Discrete Fourier Transform (DFT) pattern from the image using a linear predictor based residual spectral analysis and comparing with Expected Residual DFT pattern that is obtained using the RPC metadata associated with the image. If the metadata associated with orthorectified image is correct, then the Residual-DFT pattern (which represents image data) and the Expected-Residual-DFT pattern (which represents metadata) should be similar. We use SSIM (Structural Similarity Index Metric) to quantify the similarity and thereby verify if the data has been tampered or not. Detailed experimental results demonstrate that our method achieves over 97% accuracy in the majority of binary tampering detection tests.

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.

The advent of high-tech journaling tools facilitates an image to be manipulated in a way that can easily evade state-of-the-art image tampering detection approaches. The recent success of the deep learning approaches in different recognition tasks inspires us to develop a high confidence detection framework which can localize manipulated regions in an image. Unlike semantic object segmentation where all meaningful regions (objects) are segmented, the localization of image manipulation focuses only the possible tampered region which makes the problem even more challenging. In order to formulate the framework, we employ a hybrid CNN-LSTM model to capture discriminative features between manipulated and non-manipulated regions. One of the key properties of manipulated regions is that they exhibit discriminative features in boundaries shared with neighboring non-manipulated pixels. Our motivation is to learn the boundary discrepancy, i.e., the spatial structure, between manipulated and non-manipulated regions with the combination of LSTM and convolution layers. We perform end-to-end training of the network to learn the parameters through back-propagation given ground-truth mask information. The overall framework is capable of detecting different types of image manipulations, including copy-move, removal and splicing. Our model shows promising results in localizing manipulated regions, which is demonstrated through rigorous experimentation on three diverse datasets.

We investigate the problem of image classification within a supervised learning framework that exploits implicit mutual information in different visual features and their associated classifiers. In our proposed two stage hierarchical processing, visual features are first clustered with the objective of maximizing diversity. Majority vote within each cluster is used to enforce diversity. Many partitioning variations are evaluated using K-nearest neighbor to obtain the highest inter-cluster entropy. In the second step, a richer measure of discrimination is obtained using a fully connected conditional random fields (CRF) over clusters. The unary and interaction potentials are defined over mutual information within each cluster and inter-dependencies across clusters respectively. Experimenting over five distinct datasets, we demonstrate an average performance gain of 30% compared with state of the art techniques.

There is an alarming increase in the amount of malware that is generated today. However, several studies have shown that most of these new malware are just variants of existing ones. Fast detection of these variants plays an effective role in thwarting new attacks. In this paper, we propose a novel approach to detect malware variants using a sparse representation framework. Exploiting the fact that most malware variants have small differences in their structure, we model a new/unknown malware sample as a sparse linear combination of other malware in the training set.

In this work, we propose SigMal, a fast and precise malware detection framework based on signal processing techniques. SigMal is designed to operate with systems that process large amounts of binary samples. It has been observed that many samples received by such systems are variants of previously-seen malware, and they retain some similarity at the binary level. Previous systems used this notion of malware similarity to detect new variants of previously-seen malware. SigMal improves the state-of-the-art by leveraging techniques borrowed from signal processing to extract noise-resistant similarity signatures from the samples. SigMal uses an efficient nearest-neighbor search technique, which is scalable to millions of samples. We evaluate SigMal on 1.2 million recent samples, both packed and unpacked, observed over a duration of three months. In addition, we also used a constant dataset of known benign executables. Our results show that SigMal can classify 50% of the recent incoming samples with above 99% precision. We also show that SigMal could have detected, on average, 70 malware samples per day before any antivirus vendor detected them.

We propose a simple yet effective method for visualizing and classifying malware using image processing techniques. Malware binaries are visualized as gray-scale images, with the observation that for many malware families, the images belonging to the same family appear very similar in layout and texture. Motivated by this visual similarity, a classification method using standard image features is proposed. Neither disassembly nor code execution is required for classification. Preliminary experimental results are quite promising with 98% classification accuracy on a malware database of 9,458 samples with 25 different malware families. Our technique also exhibits interesting resilience to popular obfuscation techniques such as section encryption.

We propose a novel data hiding system where data is embedded in local non-overlapping regions in an image. To survive cropping, the encoder embeds the same data in multiple regions of fixed dimensions, while the decoder's challenge is to independently retrieve these regions. Salient feature points are computed on an image and the local regions are centered around them. To obtain non-overlapping regions, the points are pruned based on their corner strength and the size of the region. The decoder can retrieve the data only if it can precisely identify one or more of the same key-points. We present suitable key-point pruning methods such that even after considering a reduced number of key-points, the receiver is successful in identifying the same key-point locations as the encoder. We perform experimental comparison of various corner detectors and also study the performance of segmentation methods to obtain robust key-points.

A common problem affecting most image resizing detection algorithms is that they are susceptible to JPEG compression. This is because JPEG introduces periodic artifacts, as it works on 8×8 blocks. We propose a novel yet counter intuitive technique to "denoise" JPEG images by adding Gaussian noise. We add a suitable amount of Gaussian noise to a resized and JPEG compressed image so that the periodicity due to JPEG compression is suppressed while that due to resizing is retained. The controlled Gaussian noise addition works better than median filtering and weighted averaging based filtering for suppressing the JPEG induced periodicity.

Our recently introduced JPEG steganographic method called yet another steganographic scheme (YASS) can resist blind steganalysis by embedding data in the discrete cosine transform (DCT) domain in randomly chosen image blocks. To maximize the embedding rate for a given image and a specified attack channel, the redundancy factor used by the repeat- accumulate (RA) code based error correction framework in YASS is optimally chosen by the encoder. An efficient method is suggested for the decoder to accurately compute this redundancy factor. We also show experimentally which DCT coefficients are better suited for hiding and detection under various attacks. The effectiveness of YASS for robust steganography is demonstrated for certain attacks.