Successful adaptive steganography has mainly focused on embedding the payload while minimizing an appropriately defined distortion function. The application of deep learning to steganalysis has greatly challenged present adaptive steganographic methods, but has also shown the potential for the improvement of steganography. This paper proposes a distortion function generating framework for steganography. It has three modules: a generator with U-Net architecture to translate a cover image into an embedding change probability map, a no pre-training required double-tanh function to approximate the optimal embedding simulator while preserving gradient norm during backpropagation in the adversarial training, and an enhanced steganalyzer based on a convolution neural network together with multiple high pass filters as the discriminator. Extensive experimental results on different datasets have shown that the proposed framework outperforms the current state-of-the-art steganographic schemes. Moreover, the adversarial training time is reduced dramatically comparing with the GAN based automatic steganographic distortion learning framework (ASDL-GAN).

Face forgery detection is essential in combating malicious digital face attacks. Previous methods mainly rely on prior expert knowledge to capture specific forgery clues, such as noise patterns, blending boundaries, and frequency artifacts. However, these methods tend to get trapped in local optima, resulting in limited robustness and generalization capability. To address these issues, we propose a novel Critical Forgery Mining (CFM) framework, which can be flexibly assembled with various backbones to boost their generalization and robustness performance. Specifically, we first build a fine-grained triplet and suppress specific forgery traces through prior knowledge-agnostic data augmentation. Subsequently, we propose a fine-grained relation learning prototype to mine critical information in forgeries through instance and local similarity-aware losses. Moreover, we design a novel progressive learning controller to guide the model to focus on principal feature components, enabling it to learn critical forgery features in a coarse-to-fine manner. The proposed method achieves state-of-the-art forgery detection performance under various challenging evaluation settings. The source code is available at: https://github.com/LoveSiameseCat/CFM .

End-to-end technology is an active research topic in speech anti-spoofing. Although end-to-end methods have achieved remarkable success in the speech anti-spoofing, channel effects brought by telephony transmission and certain challenging forms of spoofing attacks still plague them. We observe that differences in the high-frequency components between bonafide and spoofed speech help detect some most troublesome attack forms and the differences also remain after the signals are affected by transmission and codecs. Based on this observation, we aim to utilize the high-frequency information of speech signals to develop better generalization ability to unknown attacks and stronger robustness against transmission and codecs. We propose a raw waveform processing module based on sinc convolution and multiple pre-emphasis to obtain discriminative shallow feature representations. Additionally, we propose an improved backbone to learn discriminative feature embeddings, and a feature classification loss to optimize intra-class and inter-class distances simultaneously. The above modules constitute the proposed Discriminative Frequency-information SincNet, namely DFSincNet. Our proposed algorithm demonstrates competitive performance on both ASVspoof 2019 and 2021 logical access (LA) scenarios.

Malware detection and classification are crucial for protecting digital devices and information systems. Accurate identification of malware enables researchers and incident responders to take prompt measures against malware and mitigate its damage. With the development of attention mechanisms in the field of computer vision, attention mechanism-based malware detection techniques are also rapidly evolving. The essence of the attention mechanism is to focus on the information of interest and suppress the useless information. In this paper, we develop different plug-and-play attention mechanisms based on the ResNeXt tagging model, where the designed model is trained to focus on the malware features by capturing the malware image channel perception field of view and is also able to provide more helpful and flexible information than other methods. We have named this designed neural network ResNeXt+, and its core modules are built with different plug-and-play attention mechanisms. Extensive experimental results show that ResNeXt+ is effective and efficient in malware detection and classification with high classification accuracy. The proposed methods outperform the state-of-the-art techniques with seven benchmark datasets. Cross-dataset experiments conducted on the Windows and Android datasets, with an accuracy of 90.64% on cross-dataset detection of the android. Ablation experiments are also conducted on seven datasets, which demonstrate that attention mechanisms can improve malware detection and classification accuracy.

Sensor pattern noise (SPN) has been recognized as a reliable device fingerprint for camera source identification (CSI) and image origin verification. However, the SPN extracted from a single image can be contaminated largely by image content details from scene because, for example, an image edge can be much stronger than SPN and hard to be separated. So, the identification performance is heavily dependent upon the purity of the estimated SPN. In this paper, we propose an effective SPN predictor based on eight-neighbor context-adaptive interpolation algorithm to suppress the effect of image scene and propose a source camera identification method with it to enhance the receiver operating characteristic (ROC) performance of CSI. Experimental results on different image databases and on different sizes of images show that our proposed method has the best ROC performance among all of the existing CSI schemes, as well as the best performance in resisting mild JPEG compression, especially when the false-positive rate is held low. Because trustworthy CSI must often be performed at low false-positive rates, these results demonstrate that our proposed technique is better suited for use in real-world scenarios than existing techniques. However, our proposed method needs many such as not less than 100 original images to create camera fingerprint; the advantage of the proposed method decreases when the camera fingerprint is created with less original images.