Multimodal deepfake technologies have emerged rapidly in recent years, with wide application prospects in various fields. The conventional single-training paradigm with inherent limited generalization illustrates inadequate for addressing the continuous evolution of multimodal deepfakes. However, fine-tuning a model with new deepfake data faces past forgery patterns loss and the significant domain shift in diverse novel multimodal deepfake technologies. To address these issues, we propose a novel Quantum State Analytic Incremental Adaptability Learning method (Qsaint) for multimodal deepfake detection. To stabilize prior deepfake memory, Qsaint recursively learns detection-label mapping relations for the new deepfakes artifact with a closed-form solution, preserving the distribution memory from the historical deepfake domains without accessing previous videos. During incremental learning stages, we propose a deepfake quantum state adaptability module inspired by quantum information science. It adapts to the new forgery states and aligns them with the historical deepfake knowledge through cooling and evolution operations, eliminating deepfake domain shift issues. Comprehensive experiments demonstrate that Qsaint significantly mitigates the memory interference of historical deepfakes, effectively balancing the adaptability for new forgery tasks with the memorization of known deepfake patterns.

Deepfake technology induces substantial societal challenges, establishing deepfake detection as an important area of research. However, existing research mainly relies on target deepfake datasets, which limits its generalizability across out-of-distribution tasks to some extent. Also, it often emphasizes visual modalities while neglecting the complementary information of the auditory data. Their autoregressive-based strategies also introduce long-term information interference, further constraining the detection performance. Consequently, the potential to exploit complementary relations between visual and auditory modalities and to leverage strongly correlated short-range information remains underexplored for the detection task. To address these challenges, this paper introduces Self-BiSterm, a novel self-supervised learning framework for deepfake detection. First, we propose a bidirectional synchronization distribution modeling mechanism, which calculates inconsistent distributions for video-to-audio and audio-to-video scenarios. This mechanism effectively measures audio-visual inconsistencies, improving the model's generalization performance in practical applications. Second, to mitigate the issue of long-term information distortion, we develop a short-term temporal dependency module to estimate the adjacent local receptive fields. This module facilitates the estimation of subsequent distributions by capturing short-term temporal dependencies with high precision. The effectiveness of the proposed Self-BiSterm framework is validated on various benchmarks, demonstrating superior performance compared to existing methods.

Multimodal deepfakes pose growing security threats across diverse domains, driven by rapid advancements in generative models. This demands effective Multimodal Deepfake Continual Detection (MDCD) methods capable of adapting to evolving and heterogeneous deepfake techniques. However, MDCD remains underexplored, facing two major challenges: (1) modality-specific feature disparities limit the effectiveness of simple feature fusion, exacerbating the forgetting of previous forgery-relevant knowledge; and (2) newly introduced deepfake videos initially exhibit limited scale that gradually expand, causing class imbalance dominated by forged samples, undermines authentic content understanding in comming tasks. To address these issues, we propose the Analytic Synaptic Dynamic Scaling Balancer (ADanser) that adapts to modality-specific biases and class imbalance while employing a closed-form update to preserve prior multimodal deepfake knowledge in an evolving data stream. Inspired by synaptic scaling in neuroscience, ADanser introduces a modality synaptic scaling mechanism that applies modality-aware attention to extract discriminative and complementary forgery patterns, improving cross-modal knowledge retention. Additionally, a class-wise contribution balancer dynamically reweights learning signals to reduce class bias and enhance authentic video representation. Extensive experiments on benchmark multimodal deepfake datasets demonstrate that ADanser significantly outperforms state-of-the-art continual learning methods, effectively coordinating adaptation and retention in imbalanced, cross-modal scenarios.

Various types of network protocol software vulnerabilities often result in considerable damage. However, existing root cause analysis methods, which rely on symbolic path tracing and the hardware processor tracing (PT) function, cannot be applied in protocol software. They are also limited by the restricted resources of embedded platforms and symbolic execution ability. Additionally, manually analysing vulnerabilities is typically labour intensive. To solve this problem, we propose SEEKER, the first root cause analysis method based on deterministic replay for multi-type network protocol vulnerabilities to automatically generate vulnerability analysis reports. By proposing a multilayer semantic model, SEEKER extracts fine-grained semantics, compares the extracted semantics with predefined vulnerability rules and finally generates an analysis report.We implemented and evaluated SEEKER against 7 vulnerability types, across 4 real-world software programs, covering 2 different platforms. The experimental results show that SEEKER can identify the root causes of multi-type vulnerabilities and even find 3 new 0-day vulnerabilities. Meanwhile, SEEKER demonstrates impressive adaptability and scalability. It can analyse one execution path that involves up to 135,437,793 instructions and upwards of 15,893,356 memory access requests.