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Found 26 papers, 8 papers with code
Wasserstein Selective Transfer Learning for Cross-domain Text Mining
However, the source and target domains usually have different data distributions, which may lead to negative transfer.
Organic or Diffused: Can We Distinguish Human Art from AI-generated Images?
We curate real human art across 7 styles, generate matching images from 5 generative models, and apply 8 detectors (5 automated detectors and 3 different human groups including 180 crowdworkers, 4000+ professional artists, and 13 expert artists experienced at detecting AI).
Towards Scalable and Robust Model Versioning
In this paper, we explore the feasibility of generating multiple versions of a model that possess different attack properties, without acquiring new training data or changing model architecture.
Prompt-Specific Poisoning Attacks on Text-to-Image Generative Models
In this paper, we show that poisoning attacks can be successful on generative models.
Investigating Graph Structure Information for Entity Alignment with Dangling Cases
To improve the exploitation of the structural information, we propose a novel entity alignment framework called Weakly-Optimal Graph Contrastive Learning (WOGCL), which is refined on three dimensions : (i) Model.
Characterizing the Optimal 0-1 Loss for Multi-class Classification with a Test-time Attacker
Finding classifiers robust to adversarial examples is critical for their safe deployment.
Linguistic Rules-Based Corpus Generation for Native Chinese Grammatical Error Correction
Extensive experiments and detailed analyses not only demonstrate that the training data constructed by our method effectively improves the performance of CGEC models, but also reflect that our benchmark is an excellent resource for further development of the CGEC field.
Learning from the Dictionary: Heterogeneous Knowledge Guided Fine-tuning for Chinese Spell Checking
Chinese Spell Checking (CSC) aims to detect and correct Chinese spelling errors.
Natural Backdoor Datasets
Research on physical backdoors is limited by access to large datasets containing real images of physical objects co-located with targets of classification.
Understanding Robust Learning through the Lens of Representation Similarities
Representation learning, i. e. the generation of representations useful for downstream applications, is a task of fundamental importance that underlies much of the success of deep neural networks (DNNs).
On the Permanence of Backdoors in Evolving Models
Existing research on training-time attacks for deep neural networks (DNNs), such as backdoors, largely assume that models are static once trained, and hidden backdoors trained into models remain active indefinitely.
Global Mixup: Eliminating Ambiguity with Clustering
(2) linear combination significantly limits the sampling space for generating samples.
Assessing Privacy Risks from Feature Vector Reconstruction Attacks
In deep neural networks for facial recognition, feature vectors are numerical representations that capture the unique features of a given face.
SoK: Anti-Facial Recognition Technology
The rapid adoption of facial recognition (FR) technology by both government and commercial entities in recent years has raised concerns about civil liberties and privacy.
Poison Forensics: Traceback of Data Poisoning Attacks in Neural Networks
We propose a novel iterative clustering and pruning solution that trims "innocent" training samples, until all that remains is the set of poisoned data responsible for the attack.
"Hello, It's Me": Deep Learning-based Speech Synthesis Attacks in the Real World
Advances in deep learning have introduced a new wave of voice synthesis tools, capable of producing audio that sounds as if spoken by a target speaker.
ASR-GLUE: A New Multi-task Benchmark for ASR-Robust Natural Language Understanding
%To facilitate the research on ASR-robust general language understanding, In this paper, we propose ASR-GLUE benchmark, a new collection of 6 different NLU tasks for evaluating the performance of models under ASR error across 3 different levels of background noise and 6 speakers with various voice characteristics.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Understanding the Effect of Bias in Deep Anomaly Detection
We also provide an extensive empirical study on how a biased training anomaly set affects the anomaly score function and therefore the detection performance on different anomaly classes.
A Real-time Defense against Website Fingerprinting Attacks
We experimentally demonstrate that Dolos provides 94+% protection against state-of-the-art WF attacks under a variety of settings.
Website Fingerprinting Attacks
Cryptography and Security
Understanding Bias in Anomaly Detection: A Semi-Supervised View with PAC Guarantees
Given two different anomaly score functions, we formally define their difference in performance as the relative scoring bias of the anomaly detectors.
Semi-supervised Anomaly Detection
Supervised Anomaly Detection
+1
Answer-driven Deep Question Generation based on Reinforcement Learning
First, we propose an answer-aware initialization module with a gated connection layer which introduces both document and answer information to the decoder, thus helping to guide the choice of answer-focused question words.
Integrating User History into Heterogeneous Graph for Dialogue Act Recognition
Dialogue Act Recognition (DAR) is a challenging problem in Natural Language Understanding, which aims to attach Dialogue Act (DA) labels to each utterance in a conversation.
Summarize before Aggregate: A Global-to-local Heterogeneous Graph Inference Network for Conversational Emotion Recognition
Local dependencies, which captures short-term emotional effects between neighbouring utterances, are further injected via an Aggregation Graph to distinguish the subtle differences between utterances containing emotional phrases.
Ranked #29 on
Emotion Recognition in Conversation
on IEMOCAP
Backdoor Attacks Against Deep Learning Systems in the Physical World
A critical question remains unanswered: can backdoor attacks succeed using physical objects as triggers, thus making them a credible threat against deep learning systems in the real world?
Piracy Resistant Watermarks for Deep Neural Networks
We empirically show that our proposed watermarks achieve piracy resistance and other watermark properties, over a wide range of tasks and models.
Et Tu Alexa? When Commodity WiFi Devices Turn into Adversarial Motion Sensors
Our work demonstrates a new set of silent reconnaissance attacks, which leverages the presence of commodity WiFi devices to track users inside private homes and offices, without compromising any WiFi network, data packets, or devices.
Cryptography and Security
