Fast, automatic and fine-grained tampered JPEG image detection via DCT coefficient analysis

The quick advance in image/video editing techniques has enabled people to synthesize realistic images/videos conveniently. Some legal issues may arise when a tampered image cannot be distinguished from a real one by visual examination. In this paper, we focus on JPEG images and propose detecting tampered images by examining the double quantization effect hidden among the discrete cosine transform (DCT) coefficients. To our knowledge, our approach is the only one to date that can automatically locate the tampered region, while it has several additional advantages: fine-grained detection at the scale of 8×8 DCT blocks, insensitivity to different kinds of forgery methods (such as alpha matting and inpainting, in addition to simple image cut/paste), the ability to work without fully decompressing the JPEG images, and the fast speed. Experimental results on JPEG images are promising.     

A new data fusion model for high spatial- and temporal-resolution mapping of forest disturbance based on Landsat and MODIS

Investigating the temporal and spatial pattern of landscape disturbances is an important requirement for modeling ecosystem characteristics, including understanding changes in the terrestrial carbon cycle or mapping the quality and abundance of wildlife habitats. Data from the Landsat series of satellites have been successfully applied to map a range of biophysical vegetation parameters at a 30 m spatial resolution; the Landsat 16 day revisit cycle, however, which is often extended due to cloud cover, can be a major obstacle for monitoring short term disturbances and changes in vegetation characteristics through time.The development of data fusion techniques has helped to improve the temporal resolution of fine spatial resolution data by blending observations from sensors with differing spatial and temporal characteristics. This study introduces a new data fusion model for producing synthetic imagery and the detection of changes termed Spatial Temporal Adaptive Algorithm for mapping Reflectance Change (STAARCH). The algorithm is designed to detect changes in reflectance, denoting disturbance, using Tasseled Cap transformations of both Landsat TM/ETM and MODIS reflectance data. The algorithm has been tested over a 185 × 185 km study area in west-central Alberta, Canada. Results show that STAARCH was able to identify spatial and temporal changes in the landscape with a high level of detail. The spatial accuracy of the disturbed area was 93% when compared to the validation data set, while temporal changes in the landscape were correctly estimated for 87% to 89% of instances for the total disturbed area. The change sequence derived from STAARCH was also used to produce synthetic Landsat images for the study period for each available date of MODIS imagery. Comparison to existing Landsat observations showed that the change sequence derived from STAARCH helped to improve the prediction results when compared to previously published data fusion techniques.     

Effects of spectral response function on surface reflectance and NDVI measured with moderate resolution satellite sensors: Extension to AVHRR NOAA-17, 18 and METOP-A

This work extends the previous study of Trishchenko et al. [Trishchenko, A. P., Cihlar, J., & Li, Z. (2002). Effects of spectral response function on surface reflectance and NDVI measured with moderate resolution satellite sensors. Remote Sensing of Environment 81 (1), 1-18] that analyzed the spectral response function (SRF) effect for the Advanced Very High Resolution Radiometer (AVHRR) onboard the NOAA satellites NOAA-6 to NOAA-16 as well as the Moderate Resolution Imaging Spectroradiometer (MODIS), the VEGETATION sensor (VGT) and the Global Imager (GLI). The developed approach is now applied to cover three new AVHRR sensors launched in recent years on NOAA-17, 18, and METOP-A platforms. As in the previous study, the results are provided relative to the reference sensor AVHRR NOAA-9. The differences in reflectance among these three radiometers relative to the AVHRR NOAA-9 are similar to each other and range from − 0.015 to 0.015 (− 20% to + 2% relative) for visible (red) channel, and from − 0.03 to 0.02 (− 5% to 5%) for the near infrared (NIR) channel. The absolute change in the Normalized Difference Vegetation Index (NDVI) ranged from − 0.03 to + 0.06. Due to systematic biases of the visible channels toward smaller values and the NIR channels toward slightly larger values, the overall systematic biases for NDVI are positive. The polynomial approximations are provided for the bulk spectral correction with respect to the AVHRR NOAA-9 for consistency with previous study. Analysis was also conducted for the SRF effect only among the AVHRR-3 type of radiometer on NOAA-15, 16, 17, 18 and METOP-A using AVHRR NOAA-18 as a reference. The results show more consistency between sensors with typical correction being under 5% (or 0.01 in absolute values). The AVHRR METOP-A reveals the most different behavior among the AVHRR-3 group with generally positive bias for visible channel (up to + 5%, relative), slightly negative bias for the NIR channel (1%-2% relative), and negative NDVI bias (− 0.02 to + 0.005). Polynomial corrections are also suggested for normalization of AVHRR on NOAA-15, 16, 17 and METOP-A to AVHRR NOAA-18.     

Potential of near-field microwave imaging in breast cancer detection utilizing tapered rectangular waveguide probes

Microwave imaging for medical applications has been of interest for many years. A novel near-field microwave non-invasive testing and evaluation (NIT&E) technique utilizing tapered rectangular waveguide probes is presented for breast cancer detection. Near-field microwave NIT&E techniques can be a successful candidate for the detection of breast cancer because of their potential in dealing with materials of low conductivity (i.e. lossy dielectrics like the breast tissue). The physical basis for breast tumor detection with microwave imaging is the contrast in dielectric properties of normal and malignant breast tissues. A method adopting Fourier transform matching (FTM) technique and utilizing the reflection coefficient at the aperture of a tapered rectangular waveguide sensor radiating into a breast is described resulting in microwave images that indicate the presence of a tumor. These images demonstrate the feasibility of detecting breast tumors using this approach.     

A TCAM-based solution for integrated traffic anomaly detection and policy filtering

The survivability of the future Internet is largely dependent on whether it will be able to successfully address both security and performance issues facing the Internet. On one hand, the Internet becomes more and more vulnerable due to fast spreading malicious attacks. On the other hand, it is under great stress to meet ever growing/changing application demands while having to sustain multi-gigabit forwarding performance. In this paper, we propose a Ternary Content Addressable Memory (TCAM) coprocessor based solution for high speed, integrated TCP flow anomaly detection and policy filtering. The attacking packets with spoofed source IP addresses are detected through two-dimensional (2D) matching. The key features of the solution are: (1) setting flag bits in TCAM action code to support various packet treatments; (2) managing TCP flow state in pair to do 2D matching. We evaluate the solution’s ability to detect TCP-based flooding attacks based on real-world-trace simulations. The results show that the proposed solution can match up OC-192 line rate. The possible modifications of the solution for the detection of low rate TCP-targeted attacks are also discussed.     

Building lightweight intrusion detection system using wrapper-based feature selection mechanisms

Intrusion Detection System (IDS) is an important and necessary component in ensuring network security and protecting network resources and network infrastructures. How to build a lightweight IDS is a hot topic in network security. Moreover, feature selection is a classic research topic in data mining and it has attracted much interest from researchers in many fields such as network security, pattern recognition and data mining. In this paper, we effectively introduced feature selection methods to intrusion detection domain. We propose a wrapper-based feature selection algorithm aiming at building lightweight intrusion detection system by using modified random mutation hill climbing (RMHC) as search strategy to specify a candidate subset for evaluation, as well as using modified linear Support Vector Machines (SVMs) iterative procedure as wrapper approach to obtain the optimum feature subset. We verify the effectiveness and the feasibility of our feature selection algorithm by several experiments on KDD Cup 1999 intrusion detection dataset. The experimental results strongly show that our approach is not only able to speed up the process of selecting important features but also to yield high detection rates. Furthermore, our experimental results indicate that intrusion detection system with feature selection algorithm has better performance than that without feature selection algorithm both in detection performance and computational cost.     

IP-LSSVM: A two-step sparse classifier

We present in this work a two-step sparse classifier called IP-LSSVM which is based on Least Squares Support Vector Machine (LS-SVM). The formulation of LS-SVM aims at solving the learning problem with a system of linear equations. Although this solution is simpler, there is a loss of sparseness in the feature vectors. Many works on LS-SVM are focused on improving support vectors representation in the least squares approach, since they correspond to the only vectors that must be stored for further usage of the machine, which can also be directly used as a reduced subset that represents the initial one. The proposed classifier incorporates the advantages of either SVM and LS-SVM: automatic detection of support vectors and a solution obtained simply by the solution of systems of linear equations. IP-LSSVM was compared with other sparse LS-SVM classifiers from literature, and RRS+LS-SVM. The experiments were performed on four important benchmark databases in Machine Learning and on two artificial databases created to show visually the support vectors detected. The results show that IP-LSSVM represents a viable alternative to SVMs, since both have similar features, supported by literature results and yet IP-LSSVM has a simpler and more understandable formulation.     

Exploiting an antivirus interface

We propose a technique for defeating signature-based malware detectors by exploiting information disclosed by antivirus interfaces. This information is leveraged to reverse engineer relevant details of the detector's underlying signature database, revealing binary obfuscations that suffice to conceal malware from the detector. Experiments with real malware and antivirus interfaces on Windows operating systems justify the effectiveness of our approach.     

Automatic detection of breast cancers in mammograms using structured support vector machines

Breast cancer is one of the most common cancers diagnosed in women. Large margin classifiers like the support vector machine (SVM) have been reported effective in computer-assisted diagnosis systems for breast cancers. However, since the separating hyperplane determination exclusively relies on support vectors, the SVM is essentially a local classifier and its performance can be further improved. In this work, we introduce a structured SVM model to determine if each mammographic region is normal or cancerous by considering the cluster structures in the training set. The optimization problem in this new model can be solved efficiently by being formulated as one second order cone programming problem. Experimental evaluation is performed on the Digital Database for Screening Mammography (DDSM) dataset. Various types of features, including curvilinear features, texture features, Gabor features, and multi-resolution features, are extracted from the sample images. We then select the salient features using the recursive feature elimination algorithm. The structured SVM achieves better detection performance compared with a well-tested SVM classifier in terms of the area under the ROC curve.