The protection of cyber Intellectual Property (IP) such as web content is an increasingly critical concern.The rise of large language models (LLMs) with online retrieval capabilities enables convenient access to information but often undermines the rights of original content creators.As users increasingly rely on LLM-generated responses, they gradually diminish direct engagement with original information sources, which will significantly reduce the incentives for IP creators to contribute, and lead to a saturating cyberspace with more AIgenerated content.In response, we propose a novel defense framework that empowers web content creators to safeguard their web-based IP from unauthorized LLM real-time extraction and redistribution by leveraging the semantic understanding capability of LLMs themselves.Our method follows principled motivations and effectively addresses an intractable black-box optimization problem.Real-world experiments demonstrated that our methods improve defense success rates from 2.5% to 88.6% on different LLMs, outperforming traditional defenses such as configuration-based restrictions.

The MIS2-TrueFact is geared towards bringing academic, industry, and government researchers and practitioners together to tackle the challenges in misinformation, misbehavior, and data quality issues on the web with heterogeneous and multi-modal sources of information including texts, images, videos, relational data, social networks, and knowledge graphs.

Kernel methods have been shown to be effective for many machine learning tasks such as classification and regression. In particular, support vector machines with the Gaussian kernel have proved to be powerful classification tools. The standard way to apply kernel methods is to use the kernel trick, where the inner product of the vectors in the feature space is computed via the kernel function. Using the kernel trick for SVMs, however, leads to training that is quadratic in the number of input vectors and classification that is linear with the number of support vectors. We introduce a new kernel, the CRO (Concomitant Rank Order) kernel that approximates the Gaussian kernel on the unit sphere. We also introduce a randomized feature map, called the CRO feature map that produces sparse, high-dimensional feature vectors whose inner product asymptotically equals the CRO kernel. Using the Discrete Cosine Transform for computing the CRO feature map ensures that the cost of computing feature vectors is low, allowing us to compute the feature map explicitly. Combining the CRO feature map with linear SVM we introduce the CROification algorithm which gives us the efficiency of a sparse high-dimensional linear SVM with the accuracy of the Gaussian kernel SVM.

Kernel methods have been shown to be effective for many machine learning tasks such as classification, clustering and regression. In particular, support vector machines with the RBF kernel have proved to be powerful classification tools. The standard way to apply kernel methods is to use the `kernel trick', where the inner product of the vectors in the feature space is computed via the kernel function. Using the kernel trick for SVMs, however, leads to training that is quadratic in the number of input vectors and classification that is linear with the number of support vectors. We introduce a new kernel, the CRO (Concomitant Rank Order) kernel that approximates the RBF kernel for unit length input vectors. We also introduce a new randomized feature map, based on concomitant rank order hashing, that produces sparse, high dimensional feature vectors whose inner product asymptotically equals the CRO kernel. Using the Hadamard transform for computing the CRO hashes ensures that the cost of computing feature vectors is very low. Thus, for unit length input vectors, we get the accuracy of the RBF kernel with the efficiency of a sparse high dimensional linear kernel. We show the efficacy of our approach using a number of standard datasets.

Person identification across nonoverlapping cameras, also known as person reidentification, aims to match people at different times and locations. Reidentifying people is of great importance in crucial applications such as wide-area surveillance and visual tracking. Due to the appearance variations in pose, illumination, and occlusion in different camera views, person reidentification is inherently difficult. To address these challenges, a reference-based method is proposed for person reidentification across different cameras. Instead of directly matching people by their appearance, the matching is conducted in a reference space where the descriptor for a person is translated from the original color or texture descriptors to similarity measures between this person and the exemplars in the reference set. A subspace is first learned in which the correlations of the reference data from different cameras are maximized using regularized canonical correlation analysis (RCCA). For reidentification, the gallery data and the probe data are projected onto this RCCA subspace and the reference descriptors (RDs) of the gallery and probe are generated by computing the similarity between them and the reference data. The identity of a probe is determined by comparing the RD of the probe and the RDs of the gallery. A reranking step is added to further improve the results using a saliency-based matching scheme. Experiments on publicly available datasets show that the proposed method outperforms most of the state-of-the-art approaches.

In marketing and advertising research, “zapping” is defined as the action when a viewer stops watching a commercial. Researchers analyze users' behavior in order to prevent zapping which helps advertisers to design effective commercials. Since emotions can be used to engage consumers, in this paper, we leverage automated facial expression analysis to understand consumers' zapping behavior. Firstly, we provide an accurate moment-to-moment smile detection algorithm. Secondly, we formulate a binary classification problem (zapping/non-zapping) based on real-world scenarios, and adopt smile response as the feature to predict zapping. Thirdly, to cope with the lack of a metric in advertising evaluation, we propose a new metric called Zapping Index (ZI). ZI is a moment-to-moment measurement of a user's zapping probability. It gauges not only the reaction of a user, but also the preference of a user to commercials. Finally, extensive experiments are performed to provide insights and we make recommendations that will be useful to both advertisers and advertisement publishers.

Face recognition has been studied extensively; however, real-world face recognition still remains a challenging task. The demand for unconstrained practical face recognition is rising with the explosion of online multimedia such as social networks, and video surveillance footage where face analysis is of significant importance. In this paper, we approach face recognition in the context of graph theory. We recognize an unknown face using an external reference face graph (RFG). An RFG is generated and recognition of a given face is achieved by comparing it to the faces in the constructed RFG. Centrality measures are utilized to identify distinctive faces in the reference face graph. The proposed RFG-based face recognition algorithm is robust to the changes in pose and it is also alignment free. The RFG recognition is used in conjunction with DCT locality sensitive hashing for efficient retrieval to ensure scalability. Experiments are conducted on several publicly available databases and the results show that the proposed approach outperforms the state-of-the-art methods without any preprocessing necessities such as face alignment. Due to the richness in the reference set construction, the proposed method can also handle illumination and expression variation.

Descriptors such as local binary patterns perform well for face recognition. Searching large databases using such descriptors has been problematic due to the cost of the linear search, and the inadequate performance of existing indexing methods. We present Discrete Cosine Transform (DCT) hashing for creating index structures for face descriptors. Hashes play the role of keywords: an index is created, and queried to find the images most similar to the query image. Common hash suppression is used to improve retrieval efficiency and accuracy. Results are shown on a combination of six publicly available face databases (LFW, FERET, FEI, BioID, Multi-PIE, and RaFD). It is shown that DCT hashing has significantly better retrieval accuracy and it is more efficient compared to other popular state-of-the-art hash algorithms.

Person re-identification refers to recognizing people across non-overlapping cameras at different times and locations. Due to the variations in pose, illumination condition, background, and occlusion, person re-identification is inherently difficult. In this paper, we propose a reference-based method for across camera person re-identification. In the training, we learn a subspace in which the correlations of the reference data from different cameras are maximized using Regularized Canonical Correlation Analysis (RCCA). For re-identification, the gallery data and the probe data are projected into the RCCA subspace and the reference descriptors (RDs) of the gallery and probe are constructed by measuring the similarity between them and the reference data. The identity of the probe is determined by comparing the RD of the probe and the RDs of the gallery. Experiments on benchmark dataset show that the proposed method outperforms the state-of-the-art approaches.

Vehicle classification has evolved into a significant subject of study due to its importance in autonomous navigation, traffic analysis, surveillance and security systems, and transportation management. While numerous approaches have been introduced for this purpose, no specific study has been conducted to provide a robust and complete video-based vehicle classification system based on the rear-side view where the camera's field of view is directly behind the vehicle. In this paper, we present a stochastic multiclass vehicle classification system which classifies a vehicle (given its direct rear-side view) into one of four classes: sedan, pickup truck, SUV/minivan, and unknown. A feature set of tail light and vehicle dimensions is extracted which feeds a feature selection algorithm to define a low-dimensional feature vector. The feature vector is then processed by a hybrid dynamic Bayesian network to classify each vehicle. Results are shown on a database of 169 videos for four classes.