diff --git a/CHANGELOG.md b/CHANGELOG.md
index 5d921de..cc0583d 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -8,7 +8,11 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 ### In Progress
 - [Freezeable layers (encoder/decoder/etc.)](https://github.com/faceshiftlabs/DeepFaceLab/tree/feature/freezable-weights)
 
-## [1.4.1] - 2020-03-24
+## [1.4.2] - 2020-03-26
+### Fixed 
+- Fixes bug in background power with MS-SSIM, that misattributed loss from dst to src
+
+## [1.4.1] - 2020-03-25
 ### Fixed
 - When both Background Power and MS-SSIM were enabled, the src and dst losses were being overwritten with the 
   "background power" losses. Fixed so "background power" losses are properly added with the total losses.
@@ -69,7 +73,8 @@ This should help with rough areas directly next to the mask
 - Reset stale master branch to [seranus/DeepFaceLab](https://github.com/seranus/DeepFaceLab), 
   21 commits ahead of [iperov/DeepFaceLab](https://github.com/iperov/DeepFaceLab) ([compare](https://github.com/iperov/DeepFaceLab/compare/4818183...seranus:3f5ae05))
 
-[Unreleased]: https://github.com/olivierlacan/keep-a-changelog/compare/v1.4.1...HEAD
+[Unreleased]: https://github.com/faceshiftlabs/DeepFaceLab/compare/v1.4.2...HEAD
+[1.4.2]: https://github.com/faceshiftlabs/DeepFaceLab/compare/v1.4.1...v1.4.2
 [1.4.1]: https://github.com/faceshiftlabs/DeepFaceLab/compare/v1.4.0...v1.4.1
 [1.4.0]: https://github.com/faceshiftlabs/DeepFaceLab/compare/v1.3.0...v1.4.0
 [1.3.0]: https://github.com/faceshiftlabs/DeepFaceLab/compare/v1.2.1...v1.3.0
diff --git a/models/Model_SAEHD/Model.py b/models/Model_SAEHD/Model.py
index c2431c4..0d70461 100644
--- a/models/Model_SAEHD/Model.py
+++ b/models/Model_SAEHD/Model.py
@@ -518,7 +518,7 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
                     if self.options['background_power'] > 0:
                         bg_factor = self.options['background_power']
                         if self.options['ms_ssim_loss']:
-                            gpu_src_loss += bg_factor * 10 * nn.MsSsim(resolution)(gpu_target_dst, gpu_pred_dst_dst, max_val=1.0)
+                            gpu_dst_loss += bg_factor * 10 * nn.MsSsim(resolution)(gpu_target_dst, gpu_pred_dst_dst, max_val=1.0)
                         else:
                             if resolution < 256:
                                 gpu_dst_loss +=  bg_factor * tf.reduce_mean ( 10*nn.dssim(gpu_target_dst, gpu_pred_dst_dst, max_val=1.0, filter_size=int(resolution/11.6)), axis=[1])