Many deep learning synthetic speech generation tools are readily available.
The use of synthetic speech has caused financial fraud, impersonation of
people, and misinformation to spread. For this reason forensic methods that can
detect synthetic speech have been proposed. Existing methods often overfit on
one dataset and their performance reduces substantially in practical scenarios
such as detecting synthetic speech shared on social platforms. In this paper we
propose, Patched Spectrogram Synthetic Speech Detection Transformer (PS3DT), a
synthetic speech detector that converts a time domain speech signal to a
mel-spectrogram and processes it in patches using a transformer neural network.
We evaluate the detection performance of PS3DT on ASVspoof2019 dataset. Our
experiments show that PS3DT performs well on ASVspoof2019 dataset compared to
other approaches using spectrogram for synthetic speech detection. We also
investigate generalization performance of PS3DT on In-the-Wild dataset. PS3DT
generalizes well than several existing methods on detecting synthetic speech
from an out-of-distribution dataset. We also evaluate robustness of PS3DT to
detect telephone quality synthetic speech and synthetic speech shared on social
platforms (compressed speech). PS3DT is robust to compression and can detect
telephone quality synthetic speech better than several existing methods.

我们提出了一种名为 Patched Spectrogram Synthetic Speech Detection Transformer (PS3DT) 的合成语音检测器，通过将时间域语音信号转换成 mel 频谱图，并使用变压器神经网络在片段中处理该频谱图，来检测合成语音。我们评估了 PS3DT 在 ASVspoof2019 数据集上的检测性能，并研究了 PS3DT 在 In-the-Wild 数据集上的推广性能。PS3DT 对于压缩和电话质量合成语音具有较好的鲁棒性，比其他方法更好地检测电话质量合成语音。

基于修补频谱变换器的压缩鲁棒合成语音检测

Compression Robust Synthetic Speech Detection Using Patched Spectrogram  Transformer

Manipulation of biomedical images to misrepresent experimental results has
plagued the biomedical community for a while. Recent interest in the problem
led to the curation of a dataset and associated tasks to promote the
development of biomedical forensic methods. Of these, the largest manipulation
detection task focuses on the detection of duplicated regions between images.
Traditional computer-vision based forensic models trained on natural images are
not designed to overcome the challenges presented by biomedical images. We
propose a multi-scale overlap detection model to detect duplicated image
regions. Our model is structured to find duplication hierarchically, so as to
reduce the number of patch operations. It achieves state-of-the-art performance
overall and on multiple biomedical image categories.

针对生物医学图像中的错误操纵问题，我们提出一种多尺度重叠检测模型，该模型能够有效地检测生物医学图像中的重复区域，并显著提高了检测效率。

MONet：医学图像中的重复检测多尺度重叠网络

MONet: Multi-scale Overlap Network for Duplication Detection in Biomedical Images

Camera trace is a unique noise produced in digital imaging process. Most
existing forensic methods analyze camera trace to identify image origins. In
this paper, we address a new low-level vision problem, camera trace erasing, to
reveal the weakness of trace-based forensic methods. A comprehensive
investigation on existing anti-forensic methods reveals that it is non-trivial
to effectively erase camera trace while avoiding the destruction of content
signal. To reconcile these two demands, we propose Siamese Trace Erasing
(SiamTE), in which a novel hybrid loss is designed on the basis of Siamese
architecture for network training. Specifically, we propose embedded
similarity, truncated fidelity, and cross identity to form the hybrid loss.
Compared with existing anti-forensic methods, SiamTE has a clear advantage for
camera trace erasing, which is demonstrated in three representative tasks. Code
and dataset are available at this https URL

针对数字图像处理过程中产生的相机痕迹，基于 Siamese 架构设计嵌入式相似度、截断保真度、交叉身份的混合损失的 Siamese Trace Erasing (SiamTE) 方法被提出，解决了相机痕迹擦除和内容信号保护之间的关系，并在三项代表性任务中表现出相对优势。