Hash Bits Research Articles

Mobile video concept classification

Mobile content-based multimedia analysis has attracted much attention with the growing popularity of high-end mobile devices. Most previous systems focus on mobile visual search, i.e., to search images with visually duplicate or near-duplicate objects (e.g., products and landmarks). There remains a strong need for effective mobile video classification solutions, where videos that are not visually duplicate or near-duplicate but are from similar high-level semantic categories can be identified. In this work, we develop a mobile video classification system based on multi-modal analysis. On the mobile side, both visual and audio features are extracted from the input video, and these features are further compressed into compact hash bits for efficient transmission. On the server side, the received hash bits are used to compute the audio and visual Bag-of-Words representations for multi-modal concept classification. We propose a novel method where hash functions are learned based on the multi-modal information from the visual and audio codewords. Compared with traditional ways of computing visual-based and audio-based hash functions based on raw visual and audio local features separately, our method exploits the co-occurrences of audio and visual codewords as augmenting information and significantly improves the classification performance. The cost budget of our system for mobile data storage, computation, and transmission is similar to that in state-of-the-art mobile visual search systems. Extensive experiments over 10,000 YouTube videos show that our system can achieve similar classification accuracy with conventional server-based video classification systems using uncompressed raw descriptors.

Artificial Immune System for Bloom filter Optimization

filter is a probabilistic and space efficient data structure designed to check the membership of an element in a set. The trade-off to use Bloom filter may have configurable risk of false positives. The percentages of a false positive can be made low if the hash bit map is sufficiently massive. Spam is an unsolicited or irrelevant message sent on the internet to an outsized range of users or newsgroup. A spam word may be a list of well-known words that usually appear in spam mails. In the proposed system, Bin Bloom Filter (BBF) groups the words into number of bloom filters that have different false positive rates primarily based on the weights of the spam words. Clonal Selection Algorithm is one of the methods in Artificial Immune System (AIS) involved with computational methods inspired by the process of the biological immune system. This paper demonstrates the CSA algorithm for minimizing the total membership invalidation cost of the BBF which finds the optimal false positive rates and number of elements to be stored in bloom filters of Bin. The experimental results demonstrate the application of CSA in BBF and compare the results with Genetic Algorithm (GA).

Multimedia semantics-aware query-adaptive hashing with bits reconfigurability

In the past decade, locality-sensitive hashing (LSH) has gained a large amount of attention from both the multimedia and computer vision communities owing to its empirical success and theoretic guarantee in large-scale multimedia indexing and retrieval. Original LSH algorithms are designated for generic metrics such as Cosine similarity, $$\ell _2$$ -norm and Jaccard index, which are later extended to support those metrics learned from user-supplied supervision information. One of the common drawbacks of existing algorithms lies in their incapability to be flexibly adapted to the metric changes, along with the inefficacy when handling diverse semantics (e.g., the large number of semantic object categories in the ImageNet database), which motivates our proposed framework toward reconfigurable hashing. The basic idea of the proposed indexing framework is to maintain a large pool of over-complete hashing functions, which are randomly generated and shared when indexing diverse multimedia semantics. For specific semantic category, the algorithm adaptively selects the most relevant hashing bits by maximizing the consistency between semantic distance and hashing-based Hamming distance, thereby achieving reusability of the pre-computed hashing bits. Such a scheme especially benefits the indexing and retrieval of large-scale databases, since it facilitates one-off indexing rather than continuous computation-intensive maintenance toward metric adaptation. In practice, we propose a sequential bit-selection algorithm based on local consistency and global regularization. Extensive studies are conducted on large-scale image benchmarks to comparatively investigate the performance of different strategies for reconfigurable hashing. Despite the vast literature on hashing, to our best knowledge rare endeavors have been spent toward the reusability of hashing structures in large-scale data sets.

Robust image hash in Radon transform domain for authentication

Image authentication has become an emergency issue in the digital world as it can be easily tampered with the image editing techniques. In this paper, a novel robust hashing method for image authentication is proposed. The reported scheme first performs Radon transform (RT) on the image, and calculates the moment features which are invariant to translation and scaling in the projection space. Then discrete Fourier transform (DFT) is applied on the moment features to resist rotation. Finally, the magnitude of the significant DFT coefficients is normalized and quantized as the image hash bits. Experimental results show that the proposed algorithm can tolerate almost all the typical image processing manipulations, including JPEG compression, geometric distortion, blur, addition of noise, and enhancement. Compared with other approaches in the literature, the reported method is more effective for image authentication in terms of detection performance and the hash size.

Detecting superpoints through a reversible counting Bloom filter

Internet attacks such as distributed denial-of-service (DDoS) attacks and worm attacks are increasing in severity. Identifying realtime attack detection and mitigation of Internet traffic is an important and challenging problem. For example, a compromised host doing fast scanning for worm propagation often makes an unusually high number of connections to distinct destinations within a short time. We call such a host a superpoint, which are sources that connect to a large number of distinct destinations. Detecting superpoints is very important in developing effective and efficient traffic engineering schemes. We propose two novel schemes for detecting superpoints and prove guarantees on their accuracy and memory requirements. These schemes are implemented by introducing a reversible counting Bloom filter (RCBF), a special counting Bloom filter. The RCBF consists of 4 hash functions which projectively select some consecutive bits from original strings as function values. We obtain the information of superpoints using the overlapping of hash bit strings of the RCBF. The theoretical analysis and experiment results show that our schemes can precisely and efficiently detect superpoints.

Perceptual Image Hashing Based on Virtual Watermark Detection

This paper proposes a new robust and secure perceptual image hashing technique based on virtual watermark detection. The idea is justified by the fact that the watermark detector responds similarly to perceptually close images using a non embedded watermark. The hash values are extracted in binary form with a perfect control over the probability distribution of the hash bits. Moreover, a key is used to generate pseudo-random noise whose real values contribute to the randomness of the feature vector with a significantly increased uncertainty of the adversary, measured by mutual information, in comparison with linear correlation. Experimentally, the proposed technique has been shown to outperform related state-of-the art techniques recently proposed in the literature in terms of robustness with respect to image processing manipulations and geometric attacks.

Cache-, hash-, and space-efficient bloom filters

A Bloom filter is a very compact data structure that supports approximate membership queries on a set, allowing false positives. We propose several new variants of Bloom filters and replacements with similar functionality. All of them have a better cache-efficiency and need less hash bits than regular Bloom filters. Some use SIMD functionality, while the others provide an even better space efficiency. As a consequence, we get a more flexible trade-off between false-positive rate, space-efficiency, cache-efficiency, hash-efficiency, and computational effort. We analyze the efficiency of Bloom filters and the proposed replacements in detail, in terms of the false-positive rate, the number of expected cache-misses, and the number of required hash bits. We also describe and experimentally evaluate the performance of highly tuned implementations. For many settings, our alternatives perform better than the methods proposed so far.

Performance Analysis of Fridrich–Goljan Self-Embedding Authentication Method

This paper analyzes the performance of the image authentication method based on robust hashing proposed by J. Fridrich and M. Goljan . In this method, both the embedder and the detector generate the watermark from a perceptual digest of the image. Therefore, an accurate performance analysis requires assessing the relation between noise and hash bit errors. Our approach first derives the probability of hash bit error due to watermark embedding and/or the attack, and then uses such probability to derive the probabilities of false positive and false negative.

On the security of the visual hash function

Robust hash functions are central to the security of multimedia content authentication systems. Such functions are sensitive to a key but are robust to many allowed signal processing operations on the underlying content. The robustness of the hash function to changes in the original content implies the existence of a cluster in the feature space around the original contents feature vector, any point within which getting hashed to the same output. The shape and size of the cluster determines the trade-off between the robustness offered and the security of the authentication system based on the robust hash function. The clustering itself is based on a secret key and hence unknown to the attacker. However, we show that the specific clustering arrived at by the robust visual hash function (VHF) may be possible to learn. Given just an input and its hash bits, we show how to construct a statistical model of the hash function, without any knowledge of the secret key used to compute the hash. We also show how to use this model to engineer arbitrary and malicious collisions. Finally, we propose one possible modification to VHF so that constructing a model that mimics its behavior becomes difficult.