Trace Representation Research Articles

Detecting Artificial Intelligence-Generated images via deep trace representations and interactive feature fusion

The detection of Artificial Intelligence-Generated (AIG) images plays an important role in verifying the authenticity and originality of digital images. However, recent advancements in state-of-the-art image generation methods have significantly challenged the ability to differentiate AIG images from natural photographs (NP). To address this issue, a novel approach based on deep trace representations and dual-branch interactive feature fusion is presented. Firstly, a global feature extraction module that leverages attention-based MobileViT (AT-MobileViT) is designed to learn the deep representations of the global trace information. Besides, we apply multiple enhanced residual blocks to extract discriminative multi-scale features. After that, a low-level feature extraction module incorporating a channel-spatial attention (CSA) block is also carefully employed to enhance the learning of trace representations. To facilitate the capture of complementary information between features, a dual-branch interactive feature fusion module is introduced by reshaping feature vectors into interactive matrices. By conducting experiments on both seen and unseen images, results demonstrate the better performance and robustness of the proposed method.

On correlation functions in models related to the Temperley-Lieb algebra

We deal with quantum spin chains whose Hamiltonian arises from a representation of the Temperley-Lieb algebra, and we consider the mean values of those local operators which are generated by the Temperley-Lieb algebra. We present two key conjectures which relate these mean values to existing literature about factorized correlation functions in the XXZ spin chain. The first conjecture states that the finite volume mean values of the current and generalized current operators are given by the same simple formulas as in the case of the XXZ chain. The second conjecture states that the mean values of products of Temperley-Lieb generators can be factorized: they can expressed as sums of products of current mean values, such that the coefficients in the factorization depend neither on the eigenstate in question, nor on the selected representation of the algebra. The coefficients can be extracted from existing work on factorized correlation functions in the XXZ model. The conjectures should hold for all eigenstates that are non-degenerate with respect to the local charges of the models. We consider concrete representations, where we check the conjectures: the so-called golden chain, the QQ-state Potts model, and the trace representation. We also explain how to derive the generalized current operators from concrete expressions for the local charges.

Making DeepFakes More Spurious: Evading Deep Face Forgery Detection via Trace Removal Attack

DeepFakes are raising significant social concerns. Although various DeepFake detectors have been developed as forensic countermeasures, these detectors are still vulnerable to attacks. Recently, a few attacks, principally adversarial attacks, have succeeded in cloaking DeepFake images to evade detection. However, these attacks have typical detector-specific designs, which require prior knowledge about the detector, leading to poor transferability. Moreover, these attacks only consider simple security scenarios. Less is known about how effective they are in high-level scenarios where either the detectors or the attacker's knowledge varies. In this paper, we solve the above challenges with presenting a novel detector-agnostic trace removal attack for DeepFake anti-forensics. Instead of investigating the detector side, our attack looks into the original DeepFake creation pipeline, attempting to remove all detectable natural DeepFake traces to render the fake images more "authentic". To implement this attack, first, we perform a DeepFake trace discovery, identifying three discernible traces. Then a trace removal network (TR-Net) is proposed based on an adversarial learning framework involving one generator and multiple discriminators. Each discriminator is responsible for one individual trace representation to avoid cross-trace interference. These discriminators are arranged in parallel, which prompts the generator to remove various traces simultaneously. To evaluate the attack efficacy, we crafted heterogeneous security scenarios where the detectors were embedded with different levels of defense and the attackers' background knowledge of data varies. The experimental results show that the proposed attack can significantly compromise the detection accuracy of six state-of-the-art DeepFake detectors while causing only a negligible loss in visual quality to the original DeepFake samples.

XTR algorithm based encryption for Security in computer

The idea of XTR was first presented by Lenstra et al. in Crypto 2000. The efficient and compact subgroup trace representation is referred to as XTR Discrete logarithm (DLP) is considered as the basis for the security of the XTR. cipher system. It is regarded as a good substitute for the RSA and elliptic curve cryptosystems. We shall go into great detail on the mathematics underlying XTR and its uses in cryptography in this book chapter. Keywords \s• Cryptography\s• XTR

Dual-branch multi-scale densely connected network for image splicing detection and localization

In the last two decades, numerous methods have been proposed for image tampering forensics, especially when deep learning is leveraged as a powerful feature extraction technique. However, existing deep learning-based methods are not efficient to jointly explore robust representations of tampering traces from both low-level forensics patterns and high-level forensics semantics. Besides, they become unreliable for spliced regions with unknown scales in the evaluation stage. To overcome the above-mentioned limitations, a novel Dual-branch Multi-scale Densely Connected Network (DMDC-Net) is proposed in this work for image splicing detection. DMDC-Net is constructed based on an encoder-decoder framework, which equips a multi-scale dense (MD) module for feature reuse and pixel-wise feature fusion (PFF) module to leverage forensics clues from different data components adaptively. In DMDC-Net, the given input image will be fed into two individual subnets to obtain deep representation maps from RGB and high-frequency components, which contains both visual and noise inconsistencies for further analysis. Besides, the MD module is applied in Encoder of each subnet to make the model jointly learn low-level forensics patterns and high-level semantic representations, which incorporates local and global features by a multi-scale manner of dense connections. The outputs of each subnet are fused in the PFF module to take advantage of their complementarity by trainable weights and get the final detection score map. Experimental results demonstrate the effectiveness of our method and the superior performance than other state-of-the-art methods in CASIA v2.0 and Columbia dataset for different scenarios, such as post-processing operations.

The New Zealand Community Fault Model – version 1.0: an improved geological foundation for seismic hazard modelling

ABSTRACT The New Zealand Community Fault Model (NZ CFM) is a publicly available representation of New Zealand fault zones that have the potential to produce damaging earthquakes. Compiled through collaborative engagement between New Zealand earthquake-science experts, this first edition (version 1.0) of the NZ CFM builds upon previous compilations of earthquake-source active fault models with the addition of new and modified information. Developed primarily to support an update of the New Zealand National Seismic Hazard Model, the NZ CFM comprises two principal components. The first dataset is a two-dimensional map representation of the surface traces of 880 generalised fault zones. Each fault zone is assigned specific geometric and kinematic attributes, including uncertainties, supplemented with a subjective quality ranking focused primarily on the confidence in assigned slip rates. The second component is a three-dimensional representation of the fault zones as triangulated mesh surfaces that are projected down-dip from the two-dimensional mapped traces to a geophysically-defined maximum fault rupture depth. This article summarises the compilation and parameterisation of the NZ CFM, along with background on its relation to predecessor datasets, and forward applications to probabilistic seismic hazard assessment and physics-based earthquake models currently being developed for Aotearoa New Zealand.

Linear complexity and trace representation of balanced quaternary cyclotomic sequences of prime period p

Linear complexity and trace representation of balanced quaternary cyclotomic sequences of prime period p

Constructions of Complementary Sequence Sets and Complete Complementary Codes by Ideal Two-Level Autocorrelation Sequences and Permutation Polynomials

In this paper, we further investigate the constructions of complementary sequence sets (CSSs) and complete complementary codes (CCCs) by Butson-type Hadamard matrices. By taking the algebraic structure of Butson-type Hadamard (BH) matrices into consideration, we obtain the explicit representation of the δ-linear terms and δ-quadratic terms, which are ingredients to construct CSSs and CCCs. In particular, we derive the δ-quadratic terms determined by DFT matrices and BH matrices constructed from 2-level autocorrelation sequences, which yields two type of new contructions. We show that inequivalent BH matrices produce different CSSs and CCCs, which proves that our constructed CSSs and CCCs are new. As a consequence of the first type of the constructions, not only a large number of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</i> -ary CSSs and CCCs of size <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</i> ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</i> prime) have been proposed, which were never reported in the literature, but also a theory linking these CSSs of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</i> -ary sequences and the generalized Reed-Muller codes proposed by Kasami <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al</i> . is shown. These codes enjoy good error-correcting capability, tightly controlled PMEPR, and significantly extend the range of coding options for applications of OFDM using <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">pⁿ</i> subcarriers. As a consequence of the second type of the constructions, we reveal an extremely fascinating hidden connection between the sequences in aperiodic CSSs and CCCs and the sequences with ideal period 2-level autocorrelation, through their trace representations and permutation polynomials over finite fields.

Learning Polysemantic Spoof Trace: A Multi-Modal Disentanglement Network for Face Anti-spoofing

Along with the widespread use of face recognition systems, their vulnerability has become highlighted. While existing face anti-spoofing methods can be generalized between attack types, generic solutions are still challenging due to the diversity of spoof characteristics. Recently, the spoof trace disentanglement framework has shown great potential for coping with both seen and unseen spoof scenarios, but the performance is largely restricted by the single-modal input. This paper focuses on this issue and presents a multi-modal disentanglement model which targetedly learns polysemantic spoof traces for more accurate and robust generic attack detection. In particular, based on the adversarial learning mechanism, a two-stream disentangling network is designed to estimate spoof patterns from the RGB and depth inputs, respectively. In this case, it captures complementary spoofing clues inhering in different attacks. Furthermore, a fusion module is exploited, which recalibrates both representations at multiple stages to promote the disentanglement in each individual modality. It then performs cross-modality aggregation to deliver a more comprehensive spoof trace representation for prediction. Extensive evaluations are conducted on multiple benchmarks, demonstrating that learning polysemantic spoof traces favorably contributes to anti-spoofing with more perceptible and interpretable results.

A visual notation for succinct program traces

Program traces are a widely used representation for explaining the dynamic behavior of programs. They help to make sense of computations and also support the location and elimination of bugs. Unfortunately, program traces can grow quite big very quickly, even for small programs, which compromises their usefulness. In this paper we present a visual notation for program traces that supports their concise representation. We explain the design decisions of the notation and compare it in detail with several alternatives. An important part of the trace representation is its flexibility and adaptability, which allows users to transform traces by applying filters that capture common abstractions for trace representations. We also present an evaluation of the trace notation and filters on a set of standard examples. The results show that our representation can reduce the overall size of traces by at least 79%, which suggests that our notation is an effective improvement over the use of plain traces in the explanation of dynamic program behavior.

Influential Global and Local Contexts Guided Trace Representation for Fault Localization

Trace data is critical for fault localization (FL) to analyze suspicious statements potentially responsible for a failure. However, existing trace representation meets its bottleneck mainly in two aspects: (1) the trace information of a statement is restricted to a local context (i.e., a test case) without the consideration of a global context (i.e., all test cases of a test suite); (2) it just uses the ‘occurrence’ for representation without strong FL semantics. Thus, we propose UNITE : an infl U ential co N text-Gu I ded T race r E presentation, representing the trace from both global and local contexts with influential semantics for FL. UNITE embodies and implements two key ideas: (1) UNITE leverages the widely used weighting capability from local and global contexts of information retrieval to reflect how important a statement (a word) is to a test case (a document) in all test cases of a test suite (a collection), where a test case (a document) and all test cases of a test suite (a collection) represent local and global contexts respectively; (2) UNITE further elaborates the trace representation from ‘occurrence’ (weak semantics) to ‘influence’ (strong semantics) by combing program dependencies. The large-scale experiments on 12 FL techniques and 20 programs show that UNITE significantly improves FL effectiveness.

Multi-channel hypergraph topic neural network for clinical treatment pattern mining

Recently, increasing attention has been paid to mining clinical treatment patterns from electronic medical records (EMRs), which provide physicians with explicit knowledge to guide the patient’s treatment. However, the current work mainly focuses on the topic probability models. These shallow models fail to consider the complex high-order correlations. Hypergraphs provide a natural way to establish complex high-order correlations, but their potential to discover the semantics of latent topic has not been fully explored. In this paper, we integrate the topic model in hypergraph learning and propose a multi-channel hypergraph topic neural network (C3-HGTNN) to discover latent topic treatment patterns with learning high-order correlations. Specifically, the hypergraph network is constructed based on the interactions in the treatment traces, which describe the latent high-order correlations. Then, based on the heterogeneity of the nodes in the hypergraph, we generate multiple channels and perform a convolution operation on each channel to fully encode the hypergraph. Finally, we develop an extensive representation of the treatment trace and activity by combining the embeddings from various channels and generating relevant topic distributions to identify latent treatment patterns. Also, we demonstrate that our model is a form of deep inference for traditional relational topic models, filling the gap in learning high-order correlation in traditional topic models. Extensive experiments on our dataset and public dataset show the effectiveness and superiority of our model compared to the competitive baselines.

New Binary Sequences Derived from Euler Quotients Modulo pq and Their Generalizations

Pseudorandom sequences with large linear complexity can resist the linear attack. The trace representation plays an important role in analysis and design of pseudorandom sequences. In this letter, we present the construction of a family of new binary sequences derived from Euler quotients modulo pq, where pq is a product of two primes and p divides q-1. Firstly, the linear complexity of the sequences are investigated. It is proved that the sequences have larger linear complexity and can resist the attack of Berlekamp-Massey algorithm. Then, we give the trace representation of the proposed sequences by determining the corresponding defining pair. Moreover, we generalize the result to the Euler quotients modulo pmqn with m≤n. Results indicate that the generalized sequences still have high linear complexity. We also give the trace representation of the generalized sequences by determining the corresponding defining pair. The result will be helpful for the implementation and the pseudorandom properties analysis of the sequences.

Website-Aware Protocol Confusion Network for Emergent HTTP/3 Website Fingerprinting

Website fingerprinting is exploited to analyze encrypted traffic traces and infer the visited website. Existing website fingerprinting methods can achieve satisfying performance for the HTTP traffic visiting websites over TCP. Recently, a new protocol QUIC has been proposed, and HTTP-over-QUIC has been formalized as the next generation HTTP, named HTTP/3. Thus, it is necessary to classify HTTP/3 traces. However, since HTTP/3 is newly proposed and is being deployed, it is difficult to collect a large number of HTTP/3 traces. Intuitively, we can use sufficient TCP traces to improve the performance of the QUIC trace classifier. Unfortunately, the protocol discrepancy exists between TCP and QUIC traces, which undermines the generalization ability of the classifier. In this paper, for practical website fingerprinting of HTTP/3, we propose a Website-Aware Protocol Confusion Network (WAPCN), which exploits only a few QUIC traces to train a website classifier with the help of lots of available TCP traces. It consists of four main parts: a feature extractor, a website classifier, a protocol discriminator, and a website-aware adaptor. The feature extractor aims to extract trace representations from both TCP and QUIC traces. It cooperates with the website classifier to learn the discriminative representation for the website classification. The role of the protocol discriminator is to confuse protocols and guide the feature extractor to learn protocol-invariant representations. The website-aware adaptor can enhance protocol-invariant representations to be aware of the website classification boundary. Extensive experiments are conducted on various tasks to demonstrate the effectiveness of WAPCN.

Representation of Comical Traces In ‘Peter’s Articles

Born in 1898 to a learned family in Peshawar, Professor Ahmad Shah Bukhari became famous for his pen name Peter Bukhari. He was fluent in Urdu, Persian, Pashto and English. His correct pronunciation of the English language was famous. Peter Bukhari’s writings on Urdu language and literature were simultaneously humorous with essays, excellent critical essays and excellent translations, but they were not limited to the genres of literature but also became experts in journalism (serious, sound and broadcasting). Also called a diplomat. The great thing about him is that he does not allow his humour to be tainted with ridicule and satire. His humour is a pure mixture of humour and sweetness . There is not so much bitterness in it that it becomes satire and there is not so much open humour that it falls with seriousness. His subtle humour comes from his unique perspective. In addition to Peter’s fascinating essays and translations, many of his books have been published. He was a brilliant speaker, present-minded and a tycoon whose many amusing and interesting things are known. Apart from reading in the hostel, in memory of the deceased, dogs, Mabel and me, many of his interesting articles and letters are especially popular among the readers.

Correlation measures of binary sequences derived from Euler quotients

&lt;abstract&gt;&lt;p&gt;Fermat-Euler quotients arose from the study of the first case of Fermat's Last Theorem, and have numerous applications in number theory. Recently they were studied from the cryptographic aspects by constructing many pseudorandom binary sequences, whose linear complexities and trace representations were calculated. In this work, we further study their correlation measures by introducing a new approach based on Dirichlet characters, Ramanujan sums and Gauss sums. Our results show that the $ 4 $-order correlation measures of these sequences are very large. Therefore they may not be suggested for cryptography.&lt;/p&gt;&lt;/abstract&gt;

Trace Representation of &lt;i&gt;r&lt;/i&gt;-Ary Sequences Derived from Euler Quotients Modulo 2&lt;i&gt;p&lt;/i&gt;

Trace Representation of &lt;i&gt;r&lt;/i&gt;-Ary Sequences Derived from Euler Quotients Modulo 2&lt;i&gt;p&lt;/i&gt;

Remaining time prediction for business processes with concurrency based on log representation

Remaining time prediction of business processes plays an important role in resource scheduling and plan making. The structural features of single process instance and the concurrent running of multiple process instances are the main factors that affect the accuracy of the remaining time prediction. Existing prediction methods does not take full advantage of these two aspects into consideration. To address this issue, a new prediction method based on trace representation is proposed. More specifically, we first associate the prefix set generated by the event log to different states of the transition system, and encode the structural features of the prefixes in the state. Then, an annotation containing the feature representation for the prefix and the corresponding remaining time are added to each state to obtain an extended transition system. Next, states in the extended transition system are partitioned by the different lengths of the states, which considers concurrency among multiple process instances. Finally, the long short-term memory (LSTM) deep recurrent neural networks are applied to each partition for predicting the remaining time of new running instances. By extensive experimental evaluation using synthetic event logs and reallife event logs, we show that the proposed method outperforms existing baseline methods.

Co-Designers Not Troublemakers

Players in Massively Multiplayer Online Role-Playing Games (MMORPGs) generate long-standing histories with their characters, but cannot express or see traces of their adventures in the game worlds. We are interested in designing game systems where players shape and contribute their own narratives as game content. Study one designed and playtested three Virtual Tabletop Role-Playing Game (VTTRPG) prototypes where we found that structured, graphical representations of players' traces support co-design and narrative analysis. We also identified four categories of traces: environment, build, memory and object. We introduce Play Traces, a novel analysis method for representing and co-designing with players and their narratives. A structured observation including Play Traces studied 17 players over 16 four-hour sessions in the third VTTRPG prototype. We found that players successfully (and enjoyably) co-designed novel narratives. We identified three themes for how traces can affect and support players in shaping new interactive narratives. We present four design implications describing how player-created narratives in MMORPGs should first Reveal &amp; Pull Attention from other players, Invite &amp; Push further exploration, Guide &amp; Assist toward endings, and optionally Show &amp; Hide traces. Finally, we discuss how treating players as co-designers offers a promising approach for developing the next generation of MMORPGs.

Linear Complexity and Trace Representation of New Ding Generalized Cyclotomic Sequences with Period pq and Order Two

Linear complexity is an important property to measure the unpredictability of pseudo-random sequences. Trace representation is helpful for analyzing cryptography properties of pseudo-random sequences. In this paper, a class of new Ding generalized cyclotomic binary sequences of order two with period pq is constructed based on the new segmentation of Ding Helleseth generalized cyclotomy. Firstly, the linear complexity and minimal polynomial of the sequences are investigated. Then, their trace representation is given. It is proved that the sequences have larger linear complexity and can resist the attack of the Berlekamp–Massey algorithm. This paper also confirms that generalized cyclotomic sequences with good randomness may be obtained by modifying the characteristic set of generalized cyclotomy.