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Found 8 papers, 4 papers with code
An Analysis of Recent Advances in Deepfake Image Detection in an Evolving Threat Landscape
Second, the emergence of \textit{vision foundation models} -- machine learning models trained on broad data that can be easily adapted to several downstream tasks -- can be misused by attackers to craft adversarial deepfakes that can evade existing defenses.
Deepfake Text Detection: Limitations and Opportunities
Several defenses have been proposed for deepfake text detection.
Jekyll: Attacking Medical Image Diagnostics using Deep Generative Models
The potential for fraudulent claims based on such generated 'fake' medical images is significant, and we demonstrate successful attacks on both X-rays and retinal fundus image modalities.
Embedding Code Contexts for Cryptographic API Suggestion:New Methodologies and Comparisons
Despite recent research efforts, the vision of automatic code generation through API recommendation has not been realized.
T-Miner: A Generative Approach to Defend Against Trojan Attacks on DNN-based Text Classification
T-Miner employs a sequence-to-sequence (seq-2-seq) generative model that probes the suspicious classifier and learns to produce text sequences that are likely to contain the Trojan trigger.
Deepfake Videos in the Wild: Analysis and Detection
AI-manipulated videos, commonly known as deepfakes, are an emerging problem.
Throwing Darts in the Dark? Detecting Bots with Limited Data using Neural Data Augmentation
We evaluate this idea and show our method can train a model that outperforms existing methods with only 1% of the labeled data.
Automated Crowdturfing Attacks and Defenses in Online Review Systems
Malicious crowdsourcing forums are gaining traction as sources of spreading misinformation online, but are limited by the costs of hiring and managing human workers.
Cryptography and Security Social and Information Networks
