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    科学领域:

    • 计算机科学 计算机科学
    • 人工智能的人工智能
    • 图像处理 图像处理

    背景情况:

    • 深度学习推进了图像伪造本地化,但模型容易受到对抗性攻击.
    • 在对抗性攻击中不可察觉的噪音显著误导了当前的伪造本地化模型.

    研究的目的:

    • 开发一个防御机制,对抗对象攻击的图像伪造本地化.
    • 提高深度学习模型在检测操纵图像方面的稳定性.

    主要方法:

    • 引入了一个对抗噪声抑制模块 (ANSM),产生防御性扰动.
    • 实施了两阶段的培训策略:伪造相关特征对齐 (FFA) 和面具引导精细化 (MgR).
    • 使用Kullback-Leibler分歧,FFA将分配差异最小化;MgR使用双面膜约束来提高精度.

    主要成果:

    • 拟议的方法显著恢复了对抗图像的伪造本地化性能.
    • 在使用ANSM时,原始伪造图像的性能在很大程度上不受影响.
    • 广泛的实验证实了各种攻击算法的有效性.

    结论:

    • 开发的对抗噪音抑制模块 (ANSM) 提供了有效的防御对抗攻击在图像伪造本地化.
    • 这项工作代表了第一个专门针对图像伪造本地化任务的报告对抗性防御.
    • 源代码和反法医数据集被释放用于进一步研究.