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Researcher profile

Ee-Chien Chang

Ee-Chien Chang contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Cryptography and SecurityMachine LearningComputer VisionArtificial Intelligencecs.CYDistributed, Parallel, and Cluster ComputingMultimedia

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Published work

9 published item(s)

preprint2026arXiv

SnapGuard: Lightweight Prompt Injection Detection for Screenshot-Based Web Agents

Web agents have emerged as an effective paradigm for automating interactions with complex web environments, yet remain vulnerable to prompt injection attacks that embed malicious instructions into webpage content to induce unintended actions. This threat is further amplified for screenshot-based web agents, which operate on rendered visual webpages rather than structured textual representations, making predominant text-centric defenses ineffective. Although multimodal detection methods have been explored, they often rely on large vision-language models (VLMs), incurring significant computational overhead. The bottleneck lies in the complexity of modern webpages: VLMs must comprehend the global semantics of an entire page, resulting in substantial inference time and GPU memory usage. This raises a critical question: can we detect prompt injection attacks from screenshots in a lightweight manner? In this paper, we observe that injected webpages exhibit distinct characteristics compared to benign ones from both visual and textual perspectives. Building on this insight, we propose SnapGuard, a lightweight yet accurate method that reformulates prompt injection detection as multimodal representation analysis over webpage screenshots. SnapGuard leverages two complementary signals: a visual stability indicator that identifies abnormally smooth gradient distributions induced by malicious content, and action-oriented textual signals recovered via contrast-polarity reversal. Extensive evaluations across eight attacks and two benign settings demonstrate that SnapGuard achieves an F1 score of 0.75, outperforming GPT-4o-prompt while being 8x faster (1.81s vs. 14.50s) and introducing no additional memory overhead.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

De-END: Decoder-driven Watermarking Network

With recent advances in machine learning, researchers are now able to solve traditional problems with new solutions. In the area of digital watermarking, deep-learning-based watermarking technique is being extensively studied. Most existing approaches adopt a similar encoder-driven scheme which we name END (Encoder-NoiseLayer-Decoder) architecture. In this paper, we revamp the architecture and creatively design a decoder-driven watermarking network dubbed De-END which greatly outperforms the existing END-based methods. The motivation for designing De-END originated from the potential drawback we discovered in END architecture: The encoder may embed redundant features that are not necessary for decoding, limiting the performance of the whole network. We conducted a detailed analysis and found that such limitations are caused by unsatisfactory coupling between the encoder and decoder in END. De-END addresses such drawbacks by adopting a Decoder-Encoder-Noiselayer-Decoder architecture. In De-END, the host image is firstly processed by the decoder to generate a latent feature map instead of being directly fed into the encoder. This latent feature map is concatenated to the original watermark message and then processed by the encoder. This change in design is crucial as it makes the feature of encoder and decoder directly shared thus the encoder and decoder are better coupled. We conducted extensive experiments and the results show that this framework outperforms the existing state-of-the-art (SOTA) END-based deep learning watermarking both in visual quality and robustness. On the premise of the same decoder structure, the visual quality (measured by PSNR) of De-END improves by 1.6dB (45.16dB to 46.84dB), and extraction accuracy after JPEG compression (QF=50) distortion outperforms more than 4% (94.9% to 99.1%).

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Mixed Fault Tolerance Protocols with Trusted Execution Environment

Blockchain systems are designed, built and operated in the presence of failures. There are two dominant failure models, namely crash fault and Byzantine fault. Byzantine fault tolerance (BFT) protocols offer stronger security guarantees, and thus are widely used in blockchain systems. However, their security guarantees come at a dear cost to their performance and scalability. Several works have improved BFT protocols, and Trusted Execution Environment (TEE) has been shown to be an effective solution. However, existing such works typically assume that each participating node is equipped with TEE. For blockchain systems wherein participants typically have different hardware configurations, i.e., some nodes feature TEE while others do not, existing TEE-based BFT protocols are not applicable. This work studies the setting wherein not all participating nodes feature TEE, under which we propose a new fault model called mixed fault. We explore a new approach to designing efficient distributed fault-tolerant protocols under the mixed fault model. In general, mixed fault tolerance (MFT) protocols assume a network of $n$ nodes, among which up to $f = \frac{n-2}{3}$ can be subject to mixed faults. We identify two key principles for designing efficient MFT protocols, namely, (i) prioritizing non-equivocating nodes in leading the protocol, and (ii) advocating the use of public-key cryptographic primitives that allow authenticated messages to be aggregated. We showcase these design principles by prescribing an MFT protocol, namely MRaft. We implemented a prototype of MRaft using Intel SGX, integrated it into the CCF blockchain framework, conducted experiments, and showed that MFT protocols can obtain the same security guarantees as their BFT counterparts while still providing better performance (both transaction throughput and latency) and scalability.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Poisoning Online Learning Filters: DDoS Attacks and Countermeasures

The recent advancements in machine learning have led to a wave of interest in adopting online learning-based approaches for long-standing attack mitigation issues. In particular, DDoS attacks remain a significant threat to network service availability even after more than two decades. These attacks have been well studied under the assumption that malicious traffic originates from a single attack profile. Based on this premise, malicious traffic characteristics are assumed to be considerably different from legitimate traffic. Consequently, online filtering methods are designed to learn network traffic distributions adaptively and rank requests according to their attack likelihood. During an attack, requests rated as malicious are precipitously dropped by the filters. In this paper, we conduct the first systematic study on the effects of data poisoning attacks on online DDoS filtering; introduce one such attack method, and propose practical protective countermeasures for these attacks. We investigate an adverse scenario where the attacker is "crafty", switching profiles during attacks and generating erratic attack traffic that is ever-shifting. This elusive attacker generates malicious requests by manipulating and shifting traffic distribution to poison the training data and corrupt the filters. To this end, we present a generative model MimicShift, capable of controlling traffic generation while retaining the originating traffic's intrinsic properties. Comprehensive experiments show that online learning filters are highly susceptible to poisoning attacks, sometimes performing much worse than a random filtering strategy in this attack scenario. At the same time, our proposed protective countermeasure diminishes the attack impact.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Tracing the Origin of Adversarial Attack for Forensic Investigation and Deterrence

Deep neural networks are vulnerable to adversarial attacks. In this paper, we take the role of investigators who want to trace the attack and identify the source, that is, the particular model which the adversarial examples are generated from. Techniques derived would aid forensic investigation of attack incidents and serve as deterrence to potential attacks. We consider the buyers-seller setting where a machine learning model is to be distributed to various buyers and each buyer receives a slightly different copy with same functionality. A malicious buyer generates adversarial examples from a particular copy $\mathcal{M}_i$ and uses them to attack other copies. From these adversarial examples, the investigator wants to identify the source $\mathcal{M}_i$. To address this problem, we propose a two-stage separate-and-trace framework. The model separation stage generates multiple copies of a model for a same classification task. This process injects unique characteristics into each copy so that adversarial examples generated have distinct and traceable features. We give a parallel structure which embeds a ``tracer'' in each copy, and a noise-sensitive training loss to achieve this goal. The tracing stage takes in adversarial examples and a few candidate models, and identifies the likely source. Based on the unique features induced by the noise-sensitive loss function, we could effectively trace the potential adversarial copy by considering the output logits from each tracer. Empirical results show that it is possible to trace the origin of the adversarial example and the mechanism can be applied to a wide range of architectures and datasets.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Confusing and Detecting ML Adversarial Attacks with Injected Attractors

Many machine learning adversarial attacks find adversarial samples of a victim model ${\mathcal M}$ by following the gradient of some attack objective functions, either explicitly or implicitly. To confuse and detect such attacks, we take the proactive approach that modifies those functions with the goal of misleading the attacks to some local minimals, or to some designated regions that can be easily picked up by an analyzer. To achieve this goal, we propose adding a large number of artifacts, which we called $attractors$, onto the otherwise smooth function. An attractor is a point in the input space, where samples in its neighborhood have gradient pointing toward it. We observe that decoders of watermarking schemes exhibit properties of attractors and give a generic method that injects attractors from a watermark decoder into the victim model ${\mathcal M}$. This principled approach allows us to leverage on known watermarking schemes for scalability and robustness and provides explainability of the outcomes. Experimental studies show that our method has competitive performance. For instance, for un-targeted attacks on CIFAR-10 dataset, we can reduce the overall attack success rate of DeepFool to 1.9%, whereas known defense LID, FS and MagNet can reduce the rate to 90.8%, 98.5% and 78.5% respectively.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Benefits and Pitfalls of Using Capture the Flag Games in University Courses

The concept of Capture the Flag (CTF) games for practicing cybersecurity skills is widespread in informal educational settings and leisure-time competitions. However, it is not much used in university courses. This paper summarizes our experience from using jeopardy CTF games as homework assignments in an introductory undergraduate course. Our analysis of data describing students' in-game actions and course performance revealed four aspects that should be addressed in the design of CTF tasks: scoring, scaffolding, plagiarism, and learning analytics capabilities of the used CTF platform. The paper addresses these aspects by sharing our recommendations. We believe that these recommendations are useful for cybersecurity instructors who consider using CTF games for assessment in university courses and developers of CTF game frameworks.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Defending Model Inversion and Membership Inference Attacks via Prediction Purification

Neural networks are susceptible to data inference attacks such as the model inversion attack and the membership inference attack, where the attacker could infer the reconstruction and the membership of a data sample from the confidence scores predicted by the target classifier. In this paper, we propose a unified approach, namely purification framework, to defend data inference attacks. It purifies the confidence score vectors predicted by the target classifier by reducing their dispersion. The purifier can be further specialized in defending a particular attack via adversarial learning. We evaluate our approach on benchmark datasets and classifiers. We show that when the purifier is dedicated to one attack, it naturally defends the other one, which empirically demonstrates the connection between the two attacks. The purifier can effectively defend both attacks. For example, it can reduce the membership inference accuracy by up to 15% and increase the model inversion error by a factor of up to 4. Besides, it incurs less than 0.4% classification accuracy drop and less than 5.5% distortion to the confidence scores.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Enhancing Transformation-based Defenses using a Distribution Classifier

Adversarial attacks on convolutional neural networks (CNN) have gained significant attention and there have been active research efforts on defense mechanisms. Stochastic input transformation methods have been proposed, where the idea is to recover the image from adversarial attack by random transformation, and to take the majority vote as consensus among the random samples. However, the transformation improves the accuracy on adversarial images at the expense of the accuracy on clean images. While it is intuitive that the accuracy on clean images would deteriorate, the exact mechanism in which how this occurs is unclear. In this paper, we study the distribution of softmax induced by stochastic transformations. We observe that with random transformations on the clean images, although the mass of the softmax distribution could shift to the wrong class, the resulting distribution of softmax could be used to correct the prediction. Furthermore, on the adversarial counterparts, with the image transformation, the resulting shapes of the distribution of softmax are similar to the distributions from the clean images. With these observations, we propose a method to improve existing transformation-based defenses. We train a separate lightweight distribution classifier to recognize distinct features in the distributions of softmax outputs of transformed images. Our empirical studies show that our distribution classifier, by training on distributions obtained from clean images only, outperforms majority voting for both clean and adversarial images. Our method is generic and can be integrated with existing transformation-based defenses.

0 citations0 reviewsNo code linkedOpen access