Ching-Yung Lin

Rotation, scale, and translation resilient watermarking for images

Many electronic watermarks for still images and video content are sensitive to geometric distortions. For example, simple rotation, scaling, and/or translation (RST) of an image can prevent blind detection of a public watermark. In this paper, we propose a watermarking algorithm that is robust to RST distortions. The watermark is embedded into a one-dimensional (1-D) signal obtained by taking the Fourier transform of the image, resampling the Fourier magnitudes into log-polar coordinates, and then summing a function of those magnitudes along the log-radius axis. Rotation of the image results in a cyclical shift of the extracted signal. Scaling of the image results in amplification of the extracted signal, and translation of the image has no effect on the extracted signal. We can therefore compensate for rotation with a simple search, and compensate for scaling by using the correlation coefficient as the detection measure. False positive results on a database of 10,000 images are reported. Robustness results on a database of 2000 images are described. It is shown that the watermark is robust to rotation, scale, and translation. In addition, we describe tests examining the watermarks resistance to cropping and JPEG compression.

A robust image authentication method distinguishing JPEG compression from malicious manipulation

Image authentication verifies the originality of an image by detecting malicious manipulations. Its goal is different from that of image watermarking, which embeds into the image a signature surviving most manipulations. Most existing methods for image authentication treat all types of manipulation equally (i.e., as unacceptable). However, some practical applications demand techniques that can distinguish acceptable manipulations (e.g., compression) from malicious ones. In this paper, we present an effective technique for image authentication which can prevent malicious manipulations but allow JPEG lossy compression. The authentication signature is based on the invariance of the relationships between discrete cosine transform (DCT) coefficients at the same position in separate blocks of an image. These relationships are preserved when DCT coefficients are quantized in JPEG compression. Our proposed method can distinguish malicious manipulations from JPEG lossy compression regardless of the compression ratio or the number of compression iterations. We describe adaptive methods with probabilistic guarantee to handle distortions introduced by various acceptable manipulations such as integer rounding, image filtering, image enhancement, or scaling-recaling. We also present theoretical and experimental results to demonstrate the effectiveness of the technique.

Semifragile watermarking for authenticating JPEG visual content

In this paper, we propose a semi-fragile watermarking technique that accepts JPEG lossy compression on the watermarked image to a pre-determined quality factor, and rejects malicious attacks. The authenticator can identify the positions of corrupted blocks, and recover them with approximation of the original ones. In addition to JPEG compression, adjustments of the brightness of the image within reasonable ranges, are also acceptable using the proposed authenticator. The security of the proposed method is achieved by using the secret block mapping function which controls the signature generating/embedding processes. Our authenticator is based on two invariant properties of DCT coefficients before and after JPEG compressions. They are deterministic so that no probabilistic decision is needed in the system. The first property shows that if we modify a DCT coefficient to an integral multiple of a quantization step, which is larger than the steps used in later JPEG compressions, then this coefficient can be exactly reconstructed after later acceptable JPEG compression. The second one is the invariant relationships between two coefficients in a block pair before and after JPEG compression. Therefore, we can use the second property to generate authentication signature, and use the first property to embed it as watermarks. There is no perceptible degradation between the watermarked image and the original. In additional to authentication signatures, we can also embed the recovery bits for recovering approximate pixel values in corrupted areas. Our authenticator utilizes the compressed bitstream, and thus avoids rounding errors in reconstructing DCT coefficients. Experimental results showed the effectiveness of this system. The system also guaranies no false alarms, i.e., no acceptable JPEG compression is rejected.

Robust image authentication method surviving JPEG lossy compression

Image authentication verifies the originality of an image by detecting malicious manipulations. This goal is different from that of image watermarking which embeds into the image a signature surviving most manipulations. Existing methods for image authentication treat all types of manipulation equally (i.e., as unacceptable). However, some applications demand techniques that can distinguish acceptable manipulations (e.g., compression) from malicious ones. In this paper, we describe an effective technique for image authentication which can prevent malicious manipulations but allow JPEG lossy compression. The authentication signature is based on the invariance of the relationship between DCT coefficients of the same position in separate blocks of an image. This relationship will be preserved when these coefficients are quantized in a JPEG compression process. Our proposed method can distinguish malicious manipulations from JPEG lossy compression regardless of how high the compression ratio is. We also show that, in different practical cases, the design of authenticator depends on the number of recompression times and on whether the image is decoded into integral values in the pixel domain during the recompression process. Theoretical and experimental results indicate that this technique is effective for image authentication.

Searching for experts in the enterprise: combining text and social network analysis

Employees depend on other people in the enterprise for rapid access to important information. But current systems for finding experts do not adequately address the social implications of finding and engaging strangers in conversation. This paper provides a user study of SmallBlue, a social-context-aware expertise search system that can be used to identify experts, see dynamic profile information and get information about the social distance to the expert, before deciding whether and how to initiate contact. The system uses an innovative approach to privacy to infer content and dynamic social networks from email and chat logs. We describe usage of SmallBlue and discuss implications for the next generation of enterprise-wide systems for finding people.

Using MPEG-7 and MPEG-21 for personalizing video

As multimedia content has proliferated over the past several years, users have begun to expect that content be easily accessed according to their own preferences. One of the most effective ways to do this is through using the MPEG-7 and MPEG-21 standards, which can help address the issues associated with designing a video personalization and summarization system in heterogeneous usage environments. This three-tier architecture provides a standards-compliant infrastructure that, in conjunction with our tools, can help select, adapt, and deliver personalized video summaries to users. In extending our summarization research, we plan to explore semantic similarities across multiple simultaneous news media sources and to abstract summaries for different viewpoints. Doing so will allow us to track a semantic topic as it evolves into the future. As a result, we should be able to summarize news repositories into a smaller collection of topic threads.

Semantic Indexing of Multimedia Content Using Visual, Audio, and Text Cues

We present a learning-based approach to the semantic indexing of multimedia content using cues derived from audio, visual, and text features. We approach the problem by developing a set of statistical models for a predefined lexicon. Novel concepts are then mapped in terms of the concepts in the lexicon. To achieve robust detection of concepts, we exploit features from multiple modalities, namely, audio, video, and text. Concept representations are modeled using Gaussian mixture models (GMM), hidden Markov models (HMM), and support vector machines (SVM). Models such as Bayesian networks and SVMs are used in a late-fusion approach to model concepts that are not explicitly modeled in terms of features. Our experiments indicate promise in the proposed classification and fusion methodologies: our proposed fusion scheme achieves more than 10% relative improvement over the best unimodal concept detector.

Issues and solutions for authenticating MPEG video

Video authentication techniques are used to prove the originality of received video content and to detect malicious tampering. Existing authentication techniques protect every single bit of the video content and do not allow any form of manipulation. In real applications, this may not be practical. In several situations, compressed videos need to be further processed to accommodate various application requirements. Examples include bitrate scaling, transcoding, and frame rate conversion. The concept of asking each intermediate processing stage to add authentication codes is flawed in practical cases. In this paper, we extend our prior work on JPEG- surviving image authentication techniques to video. We first discuss issues of authenticating MPEG videos under various transcoding situations, including dynamic rate shaping, requantization, frame type conversion, and re-encoding. Different situations pose different technical challenges in developing robust authentication techniques. In the second part of this paper, we propose a robust video authentication system which accepts some MPEG transcoding processes but is able to detect malicious manipulations. It is based on unique invariant properties of the transcoding processes. Digital signature techniques as well as public key methods are used in our robust video authentication system.

Rotation, scale, and translation resilient public watermarking for images

Many electronic watermarks for still images and video content are sensitive to geometric distortions. For example, simple rotation, scaling, and/or translation (RST) of an image can prevent detection of a public watermark. In this paper, we propose a watermarking algorithm that is robust to RST distortions. The watermark is embedded into a 1-dimensional signal obtained by first taking the Fourier transform of the image, resampling the Fourier magnitudes into log-polar coordinates, and then summing a function of those magnitudes along the log-radius axis. If the image is rotated, the resulting signal is cyclically shifted. If it is scaled, the signal is multiplied by some value. And if the image is translated, the signal is unaffected. We can therefore compensate for rotation with a simple search, and for scaling by using the correlation coefficient for the detection metric. False positive results on a database of 10,000 images are reported. Robustness results on a database of 2,000 images are described. It is shown that the watermark is robust to rotation, scale and translation. In addition, the algorithm shows resistance to cropping.

Information spreading in context

Information spreading processes are central to human interactions. Despite recent studies in online domains, little is known about factors that could affect the dissemination of a single piece of information. In this paper, we address this challenge by combining two related but distinct datasets, collected from a large scale privacy-preserving distributed social sensor system. We find that the social and organizational context significantly impacts to whom and how fast people forward information. Yet the structures within spreading processes can be well captured by a simple stochastic branching model, indicating surprising independence of context. Our results build the foundation of future predictive models of information flow and provide significant insights towards design of communication platforms.