Feature Mining and Pattern Recognition in Multimedia Forensics—Detection of JPEG Image Based Steganography, Double-Compression, Interpolation and WAV Audio Based Steganography

Abstract

Steganography, the ancient art for secretive communications, has revived on the Internet by way of hiding secret data, in completely imperceptible manners, into a digital file. Thus, the steganography has created a serious threat to cyber security due to the covert channel it provides that can be readily exploited for various illegal purposes. Likewise, image tampering or forgery, which has been greatly facilitated and proliferated by photo processing tools, is increasingly causing problems concerning the authenticity of digital images. JPEG images constitute one of the most popular media on the Internet; yet they can be easily used for the steganography as well as easily tampered by, e.g., removing, adding, or splicing objects without leaving any clues. Therefore, there is a critical need to develop reliable methods for steganalysis (analysis of multimedia for the steganography) and for forgery detection in JPEG images to serve applications in national security, law enforcement, cybercrime fighting, digital forensics, and network security, etc. This article presents some recent results on detecting JPEG steganograms, doubly compressed JPEG images, and resized JPEG images based on a unified framework of feature mining and pattern recognition approaches. At first, the neighboring joint density features and marginal density features of the DCT coefficients of the JPEG image are extracted; then learning classifiers are applied to the features for the detection. Experimental results indicate that the method prominently improves the detection performances in JPEG images when compared to a previously well-studied method. Also, it is demonstrated that detection performance deteriorates with increasing image complexity; hence, a complete evaluation of the detection performance of different algorithms should include image complexity—in addition to other relevant factors such as hiding ratio or compression ratio—as a significant and independent parameter.