Beyond Text: Multimodal Jailbreaking of Vision-Language and Audio Models through Perceptually Simple Transformations

Multimodal large language models MLLMs have achieved remarkable progress, yet remain critically vulnerable to adversarial attacks that exploit weaknesses in cross-modal processing. We present a systematic study of multimodal jailbreaks targeting both vision-language and audio-language models,...

Bloodroot: When Watermarking Turns Poisonous for Stealthy Backdoor

Backdoor data poisoning is a crucial technique for ownership protection and defending against malicious attacks. Embedding hidden triggers in training data can manipulate model outputs, enabling provenance verification, and deterring unauthorized use. However, current audio backdoor methods are...

README: Robust Error-Aware Digital Signature Framework Via Deep Watermarking Model

Deep learning-based watermarking has emerged as a promising solution for robust image authentication and protection. However, existing models are limited by low embedding capacity and vulnerability to bit-level errors, making them unsuitable for cryptographic applications such as digital...

Shadow Defense against Gradient Inversion Attack in Federated Learning

Federated learning FL has emerged as a transformative framework for privacy-preserving distributed training, allowing clients to collaboratively train a global model without sharing their local data. This is especially crucial in sensitive fields like healthcare, where protecting patient data is...

[SECURITY] Fedora 34 Update: vmaf-2.1.1-1.fc34

VMAF is a perceptual video quality assessment algorithm developed by Netflix. VMAF Development Kit VDK is a software package that contains the VMAF algorithm implementation, as well as a set of tools that allows a user to train and test a custom VMAF model. For an overview, read this tech blog...

Attacking Driverless Cars with Projected Images

Interesting research -- "Phantom Attacks Against Advanced Driving Assistance Systems": Abstract: The absence of deployed vehicular communication systems, which prevents the advanced driving assistance systems ADASs and autopilots of semi/fully autonomous cars to validate their virtual perception...