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Works167 from public data
- Invisible Backdoor Attack with Sample-Specific Triggers721
Inspired by the recent advance in DNN-based image steganography, sample-specific invisible additive noises as backdoor triggers are generated by encoding an attacker-specified string into benign images through an encoder-decoder network.
- Bi-Real Net: Enhancing the Performance of 1-Bit CNNs with Improved Representational Capability and Advanced Training Algorithm662
A novel model, dubbed Bi-Real net, which connects the real activations (after the 1-bit convolution and/or BatchNorm layer, before the sign function) to activations of the consecutive block, through an identity shortcut is proposed, which achieves up to 10% higher top-1 accuracy with more memory saving and lower computational cost.
- Learning to Compose Dynamic Tree Structures for Visual Contexts623
A hybrid learning procedure is developed which integrates end-task supervised learning and the tree structure reinforcement learning, where the former's evaluation result serves as a self-critic for the latter's structure exploration.
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- Efficient Decision-Based Black-Box Adversarial Attacks on Face Recognition478
This paper evaluates the robustness of state-of-the-art face recognition models in the decision-based black-box attack setting, where the attackers have no access to the model parameters and gradients, but can only acquire hard-label predictions by sending queries to the target model.
- Target-Aware Deep Tracking445
This paper develops a regression loss and a ranking loss to guide the generation of target-active and scale-sensitive features and proposes a novel scheme to learn target-aware features, which can better recognize the targets undergoing significant appearance variations than pre-trained deep features.
- UCF: Uncovering Common Features for Generalizable Deepfake Detection341
A disentanglement framework that decomposes image information into three distinct components: forgery-irrelevant, method-specific forgery, and common forgery features is proposed and can perform superior generalization than current state-of-the-art methods.
- DeepfakeBench: A Comprehensive Benchmark of Deepfake Detection309
This work presents the first comprehensive benchmark for deepfake detection, called DeepfakeBench, which offers three key contributions: a unified data management system to ensure consistent input across all detectors, an integrated framework for state-of-the-art methods implementation, and standardized evaluation metrics and protocols to promote transparency and reproducibility.
- Transcending Forgery Specificity with Latent Space Augmentation for Generalizable Deepfake Detection237
This work proposes a simple yet effective detector called LSDA (Latent Space Data Augmentation), based on a heuristic idea: representations with a wider variety of forgeries should be able to learn a more generalizable decision boundary, thereby mitigating the overfitting of method-specific features.
- BackdoorBench: A Comprehensive Benchmark of Backdoor Learning237
A comprehensive benchmark of backdoor learning called BackdoorBench, which consists of an extensible modular-based codebase and a standardized protocol of complete backdoor learning, and provides comprehensive evaluations of every pair of 8 attacks against 9 defenses, with 5 poisoning ratios, thus 8,000 pairs of evaluations in total.
- CNN in MRF: Video Object Segmentation via Inference in a CNN-Based Higher-Order Spatio-Temporal MRF218
A novel CNN-embedded algorithm to perform approximate inference in the MRF, which outperforms the winning entries of the DAVIS 2017 Challenge, without resorting to model ensembling or any dedicated detectors.
- LAS-AT: Adversarial Training with Learnable Attack Strategy196
A novel framework for adversarial training is proposed by introducing the concept of “learnable attack strategy”, dubbed LAS-AT, which learns to automatically produce attack strategies to improve the model robustness.
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- SIDA: Social Media Image Deepfake Detection, Localization and Explanation with Large Multimodal Model176
This paper proposes a new image deepfake detection, localization, and explanation framework, named SIDA (Social media Image Detection, localization, and explanation Assistant), which not only discerns the authenticity of images, but also delineates tampered regions through mask prediction and provides textual explanations of the model’s judgment criteria.
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- BadCLIP: Dual-Embedding Guided Backdoor Attack on Multimodal Contrastive Learning145
The BadCLIP attack is introduced, which is resistant to backdoor detection and model fine-tuning defenses and draws motivations from the perspective of the Bayesian rule and proposes a dual-embedding guided framework for backdoor attacks.
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- Boosting the Transferability of Adversarial Attacks with Reverse Adversarial Perturbation136
This work proposes a novel attack method, dubbed reverse adversarial perturbation (RAP), which can find more stable adversarial examples which are less sensitive to the changes of decision boundary, mitigating the overfitting of the surrogate model.
- Backdoor Attack Against Speaker Verification132
This paper demonstrates that it is possible to inject the hidden backdoor for infecting speaker verification models by poisoning the training data and designs a clustering-based attack scheme where poisoned samples from different clusters will contain different triggers, based on the understanding of verification tasks.
- p-Box ADMM: A Versatile Framework for Integer Programming125
This paper revisits the integer programming (IP) problem, and proposes a novel and versatile framework called Alternating Direction Method of Multipliers (ADMM), which significantly outperforms existing generic IP solvers both in runtime and objective.
- Multi-label learning with missing labels for image annotation and facial action unit recognition123
This work proposes a method for multi-label learning that explicitly handles missing labels and presents an efficient algorithm to learn a parametric classifier that is applied to image annotation and facial action recognition.
- Enhancing Fine-Tuning based Backdoor Defense with Sharpness-Aware Minimization122
The FT-SAM is proposed, a novel backdoor defense paradigm that aims to shrink the norms of backdoor-related neurons by incorporating sharpness-aware minimization with fine-tuning, and provides a promising avenue for improving the robustness of machine learning models against backdoor attacks.
- Can ChatGPT Detect DeepFakes? A Study of Using Multimodal Large Language Models for Media Forensics121
The capabilities of multimodal large language models (LLMs) in DeepFake detection are investigated and it is shown that they can expose AI-generated images through careful experimental design and prompt engineering.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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