The field of deepfake detection and synthetic audio forensics is rapidly evolving, with a focus on developing innovative methods to identify and mitigate manipulated media. Recent research has explored the use of ensemble frameworks, combining transformer-based architectures and texture-based methods to achieve better detection accuracy and robustness. Additionally, there is a growing emphasis on benchmarking and evaluating detection models across diverse datasets and generation techniques. The development of new metrics and evaluation frameworks is also underway, aiming to standardize comparisons of detection systems and identify areas for improvement. Noteworthy papers in this area include:

• Forensic Similarity for Speech Deepfakes, which introduces a digital audio forensics approach to determine whether two audio segments contain the same forensic traces.
• SFANet: Spatial-Frequency Attention Network for Deepfake Detection, which proposes a novel ensemble framework for detecting manipulated media.
• Benchmarking Fake Voice Detection in the Fake Voice Generation Arms Race, which presents a large-scale, cross-domain evaluation of fake voice detectors and proposes a unified metric for evaluating detection systems.